US20090132520A1 - Combination of collaborative filtering and cliprank for personalized media content recommendation - Google Patents

Combination of collaborative filtering and cliprank for personalized media content recommendation Download PDF

Info

Publication number
US20090132520A1
US20090132520A1 US12/120,211 US12021108A US2009132520A1 US 20090132520 A1 US20090132520 A1 US 20090132520A1 US 12021108 A US12021108 A US 12021108A US 2009132520 A1 US2009132520 A1 US 2009132520A1
Authority
US
United States
Prior art keywords
media content
plurality
pieces
users
associated
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
US12/120,211
Other versions
US8010536B2 (en
Inventor
Bottyan Nemeth
Simon J. Gibbs
Mithun Sheshagiri
Priyang Rathod
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Samsung Electronics Co Ltd
Original Assignee
Samsung Electronics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US98941307P priority Critical
Application filed by Samsung Electronics Co Ltd filed Critical Samsung Electronics Co Ltd
Priority to US12/120,211 priority patent/US8010536B2/en
Assigned to SAMSUNG ELECTRONICS CO., LTD. reassignment SAMSUNG ELECTRONICS CO., LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GIBBS, SIMON J., NEMETH, BOTTYAN, RATHOD, PRIYANG, SHESHAGIRI, MITHUN
Assigned to SAMSUNG ELECTRONICS CO., LTD. reassignment SAMSUNG ELECTRONICS CO., LTD. CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE'S COUNTRY TO READ --REPUBLIC OF KOREA-- PREVIOUSLY RECORDED ON REEL 020967 FRAME 0994. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT DOCUMENT. Assignors: GIBBS, SIMON J., NEMETH, BOTTYAN, RATHOD, PRIYANG, SHESHAGIRI, MITHUN
Publication of US20090132520A1 publication Critical patent/US20090132520A1/en
Publication of US8010536B2 publication Critical patent/US8010536B2/en
Application granted granted Critical
Application status is Active legal-status Critical
Adjusted expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/48Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

Various methods for combining ClipRank and Collaborative Filtering are provided. According to one embodiment, the ClipRank weights associated with a plurality of pieces of media content are calculated based on the relationships among the plurality of pieces of media content and a plurality of users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the plurality of pieces of media content to obtain a plurality of selected pieces of media content. Collaborative Filtering is then performed on the plurality of selected pieces of media content and the plurality of users. According to another embodiment, Collaborative Filtering on a plurality of pieces of media content and a plurality of users is performed for one of the plurality of users. Personalized ClipRank weights associated with the plurality of pieces of media content is calculated for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user.

Description

    CROSS REFERENCE TO RELATED APPLICATION
  • This patent application takes priority under 35 U.S.C. 119(e) to U.S. Provisional Patent Application No. 60/989,413 (Attorney Docket No. SISAP017P/CSL07-NW14-PRO), filed on Nov. 20, 2007, entitled “A PERSONALIZED VIDEO RECOMMENDER SYSTEM” by Gibbs et al., which is hereby incorporated by reference in its entirety and for all intents and purposes.
  • FIELD OF THE INVENTION
  • The present invention generally relates to systems and methods for ranking media content, especially video content. More specifically, the present invention relates to systems and methods for ranking media content using their relationships with end users and/or with each other and in combination with Collaborative Filtering.
  • BACKGROUND OF THE INVENTION
  • Presently, there is a vast amount of media content, such as audios, videos, or graphics, available from a variety of sources. From digital graphics and music to films or movies to broadcast television programs to cable or satellite television programs to home movies or user-created video clips, there are many repositories and databases from which people may choose and obtain media content in various formats, and the amount of media content available continues to grow at a very high rate. Broadcast, cable, or satellite companies often provide hundreds of different channels for viewers to choose from. Movie rental companies such as Netflix and Blockbuster have tens, even hundreds, of thousands of titles on DVDs (digital video disc) or video cassettes. More recently, the Internet has also lent its unique capability and become a great repository and distribution channel for video media world-wide. Online stores such as Amazon.com have a great number of CDs, DVDs, and downloadable media files for sale. Websites such as YouTube and AOL Video have immense audio and video collections, often millions of audio/video clips, contributed by users from all over the world.
  • With such a great amount of available media content, often there is the need to rank a selected set of media content. For example, suppose a person is looking for video clips relating to the subject matter of figure skating at YouTube's website. The person searches for the video clips using the keywords “figure skating,” and may currently be presented with nearly sixty thousand choices. Obviously, it is impractical and nearly impossible to present all sixty thousand video clips to the person simultaneously. Instead, the video clips are presented in a sequential order, perhaps a few at a time. YouTube may choose to display twenty video clips on each web page and enable the person to examine and/or view as many video clips as he or she chooses by going through multiple web pages. In this case, the nearly sixty thousand video clips need to be ranked first so that they may be presented to the person in sequence. For example, YouTube may rank the video clips according to their relevance to the subject matter of figure skating, e.g. more relevant video clips ranked higher, or according to their posting dates, e.g., newer video clips ranked higher. Other ranking methods include ranking according to popularity, by alphabetical order, etc.
  • In another similar example, suppose a person wishes to purchase romantic music in MP3 format from Amazon. The person searches for the downloadable music files using the keywords “romance” at Amazon's website, and may currently be presented with nearly nine thousand songs. Again, the nearly nine thousand songs need to be ranked before being presented to the person in a sequential order, and the ranking may be performed according to relevance, best selling, price, average customer review, release date, etc.
  • In the above examples, although the rankings are performed for specific persons, i.e., the person searching for the video clips or the music files, the method or criteria used to rank the search results often do not take into consideration the person's own preferences or tastes. In other words, the ranking is not personally tailored for the individual users or customers. Consequently, the resulting orders may not be best suitable for the specific individuals for whom the rankings are performed.
  • SUMMARY OF THE INVENTION
  • Broadly speaking, the present invention generally relates to ranking media content using their relationships with end users and/or with each other and in combination with Collaborative Filtering. Various systems and methods for combining and/or blending ClipRank and Collaborative Filtering are provided.
  • According to one embodiment, the ClipRank weights associated with a plurality of pieces of media content are calculated based on the relationships among the plurality of pieces of media content and a plurality of users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the plurality of pieces of media content to obtain a plurality of selected pieces of media content. Collaborative Filtering is then performed on the plurality of selected pieces of media content and the plurality of users.
  • According to another embodiment, Collaborative Filtering on a plurality of pieces of media content and a plurality of users is performed for one of the plurality of users. Personalized ClipRank weights associated with the plurality of pieces of media content is calculated for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user.
  • According to another embodiment, ClipRank and Collaborative Filtering results are blended in various ways. First, general and/or personalized ClipRank weights associated with a plurality of pieces of media content based on relationships among the plurality of pieces of media content and a plurality of users. Collaborative Filtering ratings associated with the plurality of pieces of media content in connection with the plurality of users are determined. Next, the general ClipRank weights, the personalized ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content are blended in various ways. Selected pieces of media content are ranked based on the blended results of their generalized and/or personalized ClipRank weights and Collaborative Filtering ratings.
  • These and other features, aspects, and advantages of the invention will be described in more detail below in the detailed description and in conjunction with the following figures.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:
  • FIG. 1 illustrates a relationship graph between a set of users and a set of media content according to one embodiment of the invention.
  • FIG. 2 shows a method of ranking the media content using their relationships with the users according to one embodiment of the invention.
  • FIG. 3A-3D illustrate the steps of calculating the weights associated with new users and media content added to an existing relationship graph without recalculating the weights associated with users and media content already existed in the relationship graph according to one embodiment of the invention.
  • FIG. 4 is a simplified diagram illustrating a system of ranking media content using their relationships with end users according to one embodiment of the invention.
  • FIG. 5. shows a method of combining Collaborative Filtering and ClipRank by using ClipRank to select a subset of media content used for Collaborative Filtering.
  • FIG. 6 shows a method of combining Collaborative Filtering and ClipRank by using Collaborative Filtering to provide initial weight values for the media content in the relationship graph to obtain personalized ClipRank for a particular user.
  • FIG. 7 shows a method of blending Collaborative Filtering, general ClipRank, and/or personalized ClipRank.
  • FIGS. 8A and 8B illustrate a computer system 800 suitable for implementing embodiments of the present invention.
  • DETAILED DESCRIPTION OF THE INVENTION
  • The present invention will now be described in detail with reference to a few preferred embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without some or all of these specific details. In other instances, well known process steps and/or structures have not been described in detail in order to not unnecessarily obscure the present invention. In addition, while the invention will be described in conjunction with the particular embodiments, it will be understood that this description is not intended to limit the invention to the described embodiments. To the contrary, the description is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims.
  • According to various embodiments, systems and methods for ranking a set of media content using their relationships with end users and optionally, in combination with Collaborative Filtering are provided. Ranking a set of media content using their relationships with end users is sometimes referred to herein as “ClipRank.” The types of media content include, but are not limited to, audio content, video content, and/or graphics content. A relationship may exist between a user and a piece of media content, two users, or two pieces of media content. A piece of media content may be, for example, an audio, a video, or an image. There is no limit on the number of specific relationships a user may have with a piece of media content or another user, and similarly, there is no limit on the number of specific relationships a piece of media content may have with a user or another piece of media content.
  • The types of relationships may vary greatly, and their definitions depend entirely on the requirements of the specific implementations of the system and the method. For example, between a user and a piece of media content, the types of relationships may include, but not limited to, the user has created the piece of media content, the user has viewed the piece of media content, the user has recorded the piece of media content, the user has downloaded the piece of media content, the user has uploaded the piece of media content, the user has purchased the piece of media content, the user has rented the piece of media content, the user has commented on the piece of media content, the user has manually rated the piece of media content, the user has tagged the piece of media content, the user has recommended the piece of media content to at least one other user, the user has marked the piece of media content as his or her favorite, and the user is the owner of the piece of media content. Between two users, the types of relationships may include, but are not limited to: (a) the first user and the second user both belong to the same social group, (b) the first user has marked the second user as a friend, and (c) the first user has subscribed to the second user. Between two pieces of media content, the types of relationships may include, but are not limited to: (d) the first piece of media content and the second piece of media content are related, and (e) the first piece of media content and the second piece of media content both belong to the same program series.
  • Each relationship between a user and a piece of media content, between two users, and between two pieces of media content is assigned a weight. Usually, although not necessarily, relationships of the same type are assigned the same weight and the weight for each type of relationships is pre-defined. Furthermore, relatively more important types of relationships are assigned higher weights than relatively less important types of relationships. Again, what is considered a more or less type of relationships depends entirely on the requirements of the specific implementations of the system and the method. In addition, the users and the pieces of media content each has an initial weight.
  • The weights of the relationships between the users and/or the pieces of media content are used to calculate the final weights for the users and the pieces of media content, and the final weights are used to rank the users and/or the pieces of media content. Thus, the weights of the users and the pieces of media content may be referred to as “ClipRank values.” By using the weights of the relationships between the users and/or the pieces of media content to calculate the final weights for the users and the pieces of media content, which are then used to rank the users and/or the pieces of media content, the ranking results of the users and/or the pieces of media content reflect the social relationships among the users and the pieces of media content.
  • The relationships between the users and the pieces of media content may be represented using a relationship graph. FIG. 1 illustrates a relationship graph between a set of users and a set of media content according to one embodiment. For easy visual distinction, in FIG. 1, each user, denoted by “U”, is represented by a rectangular node and each piece of media content, denoted by “MC”, is represented by an oval node. However, when calculating their respective weights, the users and the pieces of media content are treated exactly the same, and no distinction is made between a user and a piece of media content for the weight calculation purpose. Each relationship, denoted by “R”, between a user, i.e., a rectangular node, and a piece of media content, i.e., an oval node, or between two users or between two pieces of media content is represented by a line, i.e., an edge, connecting the two appropriate nodes. Sometimes, the same user and piece of media content or the same two users or the same two pieces of media content may have multiple relationships of the same or different types. In this case, each specific relationship is represented by a separate line connecting the same two appropriate nodes.
  • Using the relationship graph shown in FIG. 1 as an example, there are three lines, R 148, R 149, and R 150, connecting the node U 112 and the node MC 122, suggesting that there are three separate relationships between user 112 and media content 122, e.g., a video file. The three relationships, R 148, R 149, and R 150, may be of the same type or may be of different types. For example, R 148 may represent a relationship where user 112 has viewed media content 122; R 149 may represent a relationship where user 112 has commented on media content 122; and R 150 may represent a relationship where user 122 has recommended media content 122 to another user.
  • There are two lines, R 167 and R 168, connecting the node U 114 and the node MC 133, e.g., an audio file. Again, these two lines may represent two relationships of the same type or of different types. For example, if user 114 has listened to media content 133 twice, R167 and R168 may each represents the relationship where user 114 has listened to media content 133 once.
  • There are two lines, R 160 and R 161, connecting the node U 113 and the node U 116. R 160 may represent a relationship where user 113 considers user 116 as a friend. R 161 may represent a relationship where user 116 has subscribed to user 113.
  • There is one line, R 162, connecting the node MC 126 and the node MC 131, which may represent a relationship where media content 126 and media content 131 both belong to the same program series.
  • Thus, in the relationship graph, every user and every piece of media content is represented by a node, and every relationship between a user and a piece of media content or between two users or between two pieces of media content is represented by an edge connecting the two appropriate nodes. If multiple relationships exist between a user and a piece of media content or between two users or between two pieces of media content, then multiple edges connect the two appropriate nodes, with each edge representing a specific relationship. There is no limit on the number of relationships, i.e., the number of edges, a user or a piece of media content may have, and there is no limit on the number of types of relationships that may exist between a user and a piece of media content or between two users or between two pieces of media content.
  • As indicated above, each user and each piece of media content may be associated with a weight, denoted by “W(mc_u)”, and each relationship between a user and a piece of media content or between two users or between two pieces of media content may be associated with a weight, denoted by “W(r)”. The weights associated with the relationships may be used to calculate the weights of the users and the pieces of media content. The weights of the users and/or the pieces of media content may be used to rank the users and/or the pieces of media content.
  • General ClipRank
  • According to one embodiment, the weights associated with the relationships connected to a particular media content or user are used to calculate the final weights associated with that media content or user.
  • FIG. 2 shows a method of ranking the media content using their relationships with the users according to one embodiment. A relationship graph, such as the one shown in FIG. 1, is constructed for a set of users and a set of media content (step 210). The relationship graph includes the relationships among the users and/or the media content. The information or data used to contract such a relationship graph may be obtained from various sources. For example, websites often monitor and record user actions performed at their sites. The recorded user actions may be stored in database(s) for future analysis. Thus, the stored data may be parsed to determine specific relationships among individual users and pieces of media content. More specifically, suppose a user views a video clip at a website, and the application server hosting the website monitors the viewing action from the user and records the related information in a database. Subsequently, the recorded data may be parsed to determine the identities of the user and the video clip, and the action the user has performed, i.e., viewing, with respect to the video clip. This information may then be incorporated into the relationship graph to establish a viewing relationship between the user and the video clip. Similarly, suppose a first user subscribes to a second user, e.g., the first user subscribing to the media content posted by the second user, at a website, and the subscription is recorded by the application server hosting the website. Subsequently, the recorded data may be parsed to determine the identities of the two users, and that one user has subscribed to another user. This information may then be incorporated into the relationship graph to establish a subscription relationship between the two users.
  • Once a relationship graph has been constructed, a default initial weight is assigned to each user and each piece of media content (step 220), and a pre-defined weight is assigned to each relationship among the users and the pieces of media content (step 225). Different systems may be used to represent the weight values associated with the users, the media content, and the relationships. According to some embodiments, a numerical system with a specific range is used. Any numerical system and any range may be selected. For example, the weight values may be integers between 1 to 5, 1 to 10, 1 to 100, etc.
  • Sometimes, certain relationships are considered more important than others. What relationship(s) is/are considered more or less important depends entirely on the specific requirements of a particular implementation of the system and method. Relationships that are considered important to one implementation may or may not be considered important to another implementation. Usually, although not necessarily, a relatively more important relationship is associated with a higher weight value than a relatively less important relationship. In addition, usually, although not necessarily, relationships of the same type are assigned the same weight value. The following Table 1 shows one example of the weight values associated with some relationships. The weight values are integers range from 1 to 5, and the importance of the relationships is determined based on one particular implementation.
  • TABLE 1
    Sample Weights Associated with Relationships
    Relationship Weight Value
    Relationships between a user (denoted by “U”) and a piece of media
    content (denoted by “MC”)
    U created MC 2
    U viewed MC 3
    U recorded MC 4
    U downloaded MC 3
    U uploaded MC 4
    U purchased MC 5
    U rented MC 3
    U commented on MC 1
    U manually rated MC 1
    U tagged MC 1
    U recommended MC 2
    U marked MC as favorite 3
    U owned MC 5
    Relationships between two users (denoted by “U1” and “U2”)
    U1 and U2 belong to same group 1
    U1 subscribed to U2 3
    U1 marked U2 as friend 2
    Relationships between two pieces of media content (denoted by
    “MC1” and “MC2”)
    MC1 related to MC2 2
    MC1 and MC2 belong to same series 1
  • Using the sample weights shown in Table 1, for example, each relationship in FIG. 1 would be assigned a weight value between 1 and 5. In addition, each user and each piece of media content in FIG. 1 is assigned an initial default weight value. For example, the initial default weight value for the user and media content may be 1.
  • The weights associated with the relationships are used to calculate the final weights associated with the users and pieces of media content. For each piece of media content and each user, calculate a new weight value using a predefined formula that incorporates the weights associated with the relationships connected with that piece of media content or user (step 230). The formula may be chosen based on the requirements of the specific implementations of the system and method. According to one embodiment, the weight associated with a piece of media content or a user may be calculated using the following equation:
  • W ( mc_u ) = i = 1 i = n ( W i ( r ) * W i ( mc_u ) ) ; ( 1 )
  • where W(mc_u) denotes the weight associated with the piece of media content or the user for which the weight is calculated, n denotes the total number of relationships the piece of media content or the user has with other pieces of media content or other users, Wi(r) denotes the weight associated with a relationship, relationship i, the piece of media content or the user has with another piece of media content or another user, and Wi(mc_u) denotes the weight associated with the corresponding other piece of media content, media content i, or the corresponding other user, user i, having the relationship, relationship i, with the piece of media content or the user.
  • Applying the above equation (1) to some of the nodes shown in FIG. 1 to further illustrates its usage, for example, the node representing media content 131, MC 131, has two relationships, R 162 and R 169. R 162 is connected to the node representing media content 126, MC 126; and R 169 is connected to the node representing user 116, U 116. Thus, applying equation (1) to calculate the weight value for MC 131,

  • W(MC 131)=W(R 162)*W(MC 126)+W(R 169)*W(U 116).
  • The node representing user 115, U 115, has five relationships, R 163, R 170, R 171, R 172, and R 173. R 163 is connected to the node representing media content 127, MC 127; R 170, R 171, and R 172 are all connected to the node representing media content 134, MC 134; and R 173 is connected to the node representing media content 129, MC 129. Applying equation (1) to calculate the weight value for U 115,

  • W(U 115)=W(R 163)*W(MC 127)+W(R 170)*W(MC 134)+W(R 171)*W(MC 134)+W(R 172)*W(MC 134)+W(R 173)*W(MC 129).
  • The node representing media content 120, MC 120, only has one relationship, R 140, which is connected to the node representing user 110, U 110. Applying equation (1) to calculate the weight value of MC 120,

  • W(MC 120)=W(R 140)*W(U 110).
  • Thus, by repeatedly applying the above equation (1) for each user and each piece of media content in the relationship graph, the weight values associated with each user and each piece of media content may be calculated. Note that by incorporating the weights associated with the relationships connected with a particular user or piece of media content, the weight value calculated for that user or piece of media content using equation (1) takes into consideration the social relationships among the users and pieces of media content.
  • Once the values of all the weights associated with the users and the pieces of media content have been calculated, the weight values of the users and the pieces of media content calculated during the current iteration, i.e., the current weight values, are compared with the weight values of the users and the pieces of media content calculated during the previous iteration, i.e., the previous weight values (step 240). If the difference between the current weight values and the previous weight values is less than or equal to a predefined threshold (step 250), then the weight calculation stops, and the current weight values are the final weight values for the users and the pieces of media content. Otherwise, a new iteration is repeated, such as a new set of weight values for the users and the pieces of media content are calculated (step 230).
  • There are a variety of different ways to determine the difference between the weight values calculated during the current iteration and the weight values calculated during the previous iteration. According to one embodiment, the two sums of all the weight values calculated during the two consecutive iterations may be compared. In other words, the difference between the sum of all the weight values calculated during the current iteration and the sum of all the weight values calculated during the previous iteration is compared against a predefined threshold to determine whether the weight calculation process should stop. The difference between the two sums may be calculated using the following equation:
  • difference = i = 1 i = n W i , j ( mc_u ) - i = 1 i = n W i , ( j - 1 ) ( mc_u ) , ( 2 )
  • where n denotes the total number of the pieces of media content and the users, Wi,j(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, calculated during the current iteration, iteration j, and Wi,(j-1)(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, calculated during the previous iteration, iteration j−1. Similarly, the difference between the average of all the weight values calculated during the current iteration and the average of all the weight values calculated during the previous iteration may be compared against a predefined threshold to determine whether the weight calculation process should stop. The difference between the two averages may be calculated using the following equation:
  • difference = i = 1 i = n W i , j ( mc_u ) n - i = 1 i = n W i , ( j - 1 ) ( mc_u ) n , ( 3 )
  • where, again, n denotes the total number of the pieces of media content and the users, Wi,j(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, calculated during the current iteration, iteration j, and Wi,(j-1)(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, calculated during the previous iteration, iteration j−1. The predefined threshold value may vary depending on the actual equation used.
  • According to another embodiment, instead of considering all the weights together, the difference between the weights calculated for each individual user and each individual piece of media content during the current and previous iteration may be compared separately, and the calculation process stops when each individual difference is less than or equal to a predefine threshold. For example, the threshold value may be defined as 0.1, 0.5, etc. The difference of a weight associated with a particular user or piece of media content calculated during the current iteration and the previous iteration may be calculated using the following equation:

  • difference=W i,j(mc u)−W i,(j-1)(mc u),  (4)
  • where Wi,j(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, calculated during the current iteration, iteration j, and Wi,(j-1)(mc_u) denotes the weight associated the same piece of media content, media content i, or the same user, user i, calculated during the previous iteration, iteration j−1.
  • According to another embodiment, instead of considering all the weights associated with both the users and the media content, only the difference between the weights calculated for the media content or the users calculated during the current and previous iteration is compared against a predefined threshold. For example, the difference between the two sums of the weights associated only with the media content may be calculated using the following equation:
  • difference = i = 1 i = n W i , j ( m c ) - i = 1 i = n W i , ( j - 1 ) ( m c ) , ( 5 )
  • where n denotes the total number of the pieces of media content, Wi,j(mc) denotes the weight associated a piece of media content, media content i, calculated during the current iteration, iteration j, and Wi,(j-1)(mc) denotes the weight associated a piece of media content, media content i, calculated during the previous iteration, iteration j−1. The difference between the two sums of the weights associated only with the users may be calculated using the following equation:
  • difference = i = 1 i = n W i , j ( u ) - i = 1 i = n W i , ( j - 1 ) ( u ) , ( 6 )
  • where n denotes the total number of the users, Wi,j(u) denotes the weight associated a user, user i, calculated during the current iteration, iteration j, and Wi,(j-1)(u) denotes the weight associated a user, user i, calculated during the previous iteration, iteration j−1.
  • Other embodiments may use alternative methods or formulas to determine the difference between the weight values calculated during the two consecutive, i.e., the current and the previous, iterations. Note that between one iteration and another iteration, only the weights associated with the users and the media content change, while the weights associated with the relationships remain the same. The weights calculated for the users and the media content during the previous iteration are used to calculate the weights for the users and the media content during the current iteration. Steps 230, 240, and 250 may be repeated as many times as necessary, until the difference between the weight values calculated during the current iteration and the weight values calculated during the previous iteration is less than or equal to the predefined threshold. The weights calculated during the final iteration are the final weights associated with the users and the media content.
  • The final weights are then used to rank the media content and/or the users (step 260). For example, a piece of media content with a relatively higher weight value is ranked before a piece of media content with a relatively weight value. If two pieces of media content or two users happen to have the same weight value, then a choice needs to be made as to which piece of media content or user is ranked before the other piece of media content or user. The choice may be arbitrary, or may take into consideration of some additional factors, such as the respective dates, lengths, number of relationships, etc. of the two pieces of media content.
  • The media content may be ranked separately among themselves, and the users may be ranked separately among themselves. Of course, it is also possible to rank both the media content and the users together.
  • The ranking result obtained using the method shown in FIG. 2 is not biased toward any individual user. That is, the same ranking order would result regardless of for whom the ranking is performed. Thus, this ranking result is referred to as “general ClipRank.”
  • Personalized ClipRank
  • Alternatively, the method shown in FIG. 2 may be modified slightly to obtain ranking biased toward a specific user. According to some embodiment, in step 220, instead of assigning a default value as the initial weights for the media content and the users, the initial weights assigned to the media content are determined based on data obtained from the specific user for whom the ranking is performed. The initial weights assigned to the users may still be a default value or may also be determined based on data obtained from the specific user. By doing so, the final ranking result is biased toward that user, which is referred to as “personalized ClipRank.” The other steps remain unchanged.
  • There are a variety of ways to determine initial weight values for the media content using data obtained from a specific user. For example, the user may manually specify an initial weight value for some, or possibly even all, of the pieces of media content in a relationship graph.
  • Alternatively, the initial weight values for the media content may be automatically determined based on past actions taken by the specific user in connection with the media content. According to one embodiment, the pieces of media content that have been operated on a multi-media device may be automatically rated based the actions taken by a user of the device in connection with the multi-media content. Suppose the user of the multi-media device is the specific user for whom personalized ClipRank is to be performed, then the ratings obtained for the media content from the user's multi-media device may be used to determine the initial weight values associated with the media content. There may be a direct correlation between the rating of a piece of media content and the initial weight value assigned to the piece of media content, e.g., relatively higher rating corresponding to relatively higher weight and vice versa. Automatically rating media content based on device usage information is described in more detail in co-pending U.S. patent application Ser. No. ______, (Attorney Docket No. SISAP022/CSL07-NW15), filed on ______, 2008 (concurrently herewith on the same day as the present application), entitled “SYSTEM AND METHOD FOR AUTOMATICALLY RATING VIDEO CONTENT” by Gibbs et al., which is hereby incorporated by reference in its entirety and for all intents and purposes.
  • Optionally, the initial weights assigned to the users may also be determined based on data obtained from or associated with the specific user with respect to the other users, if such data exists. For example, the specific user may manually specify and assign an initial weight value to each user. Alternatively, users that have relationships with the specific user may be assigned a higher weight than users that do not have any relationship with the specific user. If no data exists to provide initial weights for the users with respect to the specific user, the default weight value may be assigned to all the users.
  • If the initial weights assigned to the pieces of media content and optionally the users in the relationship graph are determined based on data associated with or obtained from a specific user, then the final ranking result is biased toward that user and personalized for that user.
  • Updating ClipRank Result
  • To rank a specific set of media content or a specific set of users using the method shown in FIG. 2, a relationship graph between the media content and the users, such as the one shown in FIG. 1, needs to be constructed. Thereafter, the set of media content or the set of users included in the relationship graph may be ranked based on their final weights. However, new media content and/or new users continuously become available. Thus, the relationship graph needs to be updated from time to time to include new pieces of media content and/or new users. Consequently, new weights need to be calculated and new rankings need to be conducted based on the updated relationship graph.
  • According to one embodiment, each time the relationship graph is updated, weights for all the users and media content, both old and new, are recalculated using the method shown in FIG. 2. However, this may be time-consuming, especially if the relationship graph includes a very large number of media content and users and relationships. Although the relationship graph shown in FIG. 1 only includes a few dozen nodes and edges representing the users and the media content and their relationships, in practice, a relationship graph often includes hundreds, thousands, hundreds of thousands of users, media content, and relationships. Thus, recalculating the weights for all the users and media content often may not be very efficient, especially if only a few new pieces of media content and users and their relationships are added to the relationship graph.
  • According to another embodiment, each time the relationship graph is updated, only the weights for the new users and media content are calculated. FIG. 3A-3D illustrate the steps of calculating the weights associated with the new users and media content without having to re-calculate the weights associated with the existing users and media content, which have been calculated previously.
  • FIG. 3A illustrates a sample relationship graph, which includes six nodes, MC_U 310, MC_U 311, MC_U 312, MC_U 313, MC_U 314, and MC_U 315, and eight edges, R 320, R 321, R 322, R 323, R 324, R 325, R 326, and R 327 connecting the various nodes. Each node represents a user or a piece of media content, and each edge represents a relationship between a user and a piece of media content or between two users or between two pieces of media content represented by the corresponding two nodes. To simplify the discussion, FIG. 3A only includes a small number of users, media content, and relationships, but in practice, such a relationship graph often includes a much greater number of users, media content, and relationships.
  • Suppose the weights of the nodes in FIG. 3A have been calculated using the method shown in FIG. 2. Subsequently, new users, media content, and/or relationships become available and need to be added to the relationship graph. In FIG. 3B, three new nodes, MC_U 330, MC_U 331, and MC_U 332 are added to the relationship graph shown in FIG. 3A, each node representing a user or a piece of media content. These new nodes have relationships, represented by the edges, either with some of the existing nodes or among themselves. For example, node MC_U 332 has two relationships, R 345 and R 346, with nodes MC_U 313 and MC_U 315 respectively, both being nodes already existed in the previous version of the relationship graph shown in FIG. 3A. Node MC_U 331 has two relationships, R 343 and R 344. Edge R 343 is connected with node MC_U 330, which is a new node added to the current version of the relationship graph, and edge R 344 is connected to node MC_U 311, which is a node already existed in the previous version of the relationship graph. Node MC_U 330 has four relationships, each represented by an edge. Edges R 340, R 341, and R 342 are connected with nodes MC_U 310, MC_U 312, and MC_U 311 respectively, all of which being nodes already existed in the previous version of the relationship graph. Edge R 343 is connected with node MC_U 331, which is a new node added to the current version of the relationship graph.
  • To calculate the weights associated the new nodes MC_U 330, MC_U 331, and MC_U 332 without recalculating the weights associated with the existing nodes whose weights have already been calculated, the older part of the relationship graph may be combined and collapsed into a single node. FIG. 3C illustrates collapsing the nodes and edges from the previous version of the relationship graph shown in FIG. 3A into a single combined node 350. Combined node 350 encompasses nodes MC_U 310, MC_U 311, MC_U 312, MC_U 313, MC_U 314, and MC_U 315, and edges R 320, R 321, R 322, R 323, R 324, R 325, R 326, and R 327. The weight of combined node 350 may be the average weights of the nodes included therein. In the example shown in FIG. 3C, the weight of combined node 350 equals

  • (W(MC_U 310)+W(MC_U 311)+W(MCU 312)+W(MCU 313)+W(MC_U 314)+W(MC_U 315))/6.
  • In addition, if any of the new nodes, e.g., MC_U 330, MC_U 331, or MC_U 332, have any relationships with any of the existing nodes included in combined node 350, then the relationships are now connected with combined node 350. Thus, edges R 340, R 341, R 342, R 344, R 345, and R 346 are now connected with combined node 350.
  • Hereafter, the weights of the new nodes MC_U 330, MC_U 331, and MC_U 332 may be calculated using the method shown in FIG. 2, with combined node 350 behaving like a single node. FIG. 3D shows the relationship graph that may be used to calculate the weights of the new nodes MC_U 330, MC_U 331, and MC_U 332. Again, each of the new nodes MC_U 330, MC_U 331, and MC_U 332 is assigned a default initial weight for calculating general ClipRank weights (step 220) or a default weight determined based on data associated with a specific user for calculating personalized ClipRank weights tailored to that user. Each of the relationship edges 340, R 341, R 342, R 344, R 345, and R 346 is assigned a predefined weight (step 225). Then, the weights of the nodes MC_U 330, MC_U 331, and MC_U 332 are repeated calculated under the difference between the weights calculated during the current iteration and the weights calculated during the previous iteration is less than or equal to a predefine threshold (steps 230, 240, 250).
  • Note that according to various embodiments, the weight associated with combined node 350 may be recalculated and updated, in which case it is calculated in the same manner as for the other nodes, or may remain unchanged, in which case no new weight value is calculated for combined node 350, throughout the weight calculation process. Finally, the resulting weights of the nodes may be used to rank the nodes (step 260).
  • ClipRank System Architecture
  • The ClipRank system and method may be implemented as computer program product(s) having a set of computer program instructions. The computer program instructions with which embodiments of the invention are implemented may be stored in any type of computer-readable media, and may be executed according to a variety of computing models including, for example, on a consumer electronic device, on a stand-alone computing device, or according to a distributed computing model in which various of the functionalities described herein may be effected or employed at different locations.
  • FIG. 4 is a simplified diagram illustrating a system of ranking media content using their relationships with end users according to one embodiment of the invention. One or more databases may be used to store information relating to the media content, the users, and actions the users have taken with respect to the media content and other users that may be used to determine the relationships among the users and the media content. Such information may be obtained from various sources, such as the metadata associated with the media content and users or log files recording user actions. For example, in FIG. 4, database 440 stores information associated with the media content and database 450 stores information associated with the users. The relationship graph builder 420 uses information stored in one or more databases, e.g. media content information 440 and user information 450, to construct the relationship graph 410. Another database 450 may be used to store predefined weights for various types of relationships, and the relationship graph builder 420 uses assign these predefined weights to the appropriate edges representing the various relationships in the relationship graph 410. In addition, the relationship graph builder 420 may assign general or personalized initial weights to the nodes representing the users and the media content in the relationship graph 410.
  • Once the relationship graph 410 has been constructed, the ClipRank weights calculator 430 calculate the final weights of the users and method content in the relationship graph 410 and stores the weights in database 470.
  • Combining ClipRank and Collaborative Filtering
  • ClipRank, by itself, may be used to rank a set of media content and/or users in a variety of applications, especially where it is desirable for the ranking results to take into consideration the relationships among the media content and the users. Alternatively, ClipRank, both general and personalized, may be combined with Collaborative Filtering to rank a selected set of media content.
  • Collaborative Filtering (CF) is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Collaborative Filtering typically yield more accurate results when applied to very large data sets, such as data sets including hundreds, thousands, or millions of data points. As applied to ranking a set of media content, Collaborative Filtering may be used to calculate or estimate a ranking or weight value a particular user would give a particular piece of media content that is unknown to the user based on other ratings the user has given to other pieces of media content in the past and ratings given by other users to various pieces of media content.
  • There are various algorithms that may be used to calculate Collaborative Filtering values, and specific embodiments may select different algorithms that are suitable for their specific requirements. To briefly explain the general concept of Collaborative Filtering, suppose with respect to a group of users and a set of media content, selected users have interacted with and rated selected pieces of media content. These relationships between the group of users and the set of media content may be represented by a matrix. The following Table 2 shows one example of the relationship matrix between a group of users, denoted by U1 to U8, and a set of media content, denoted by MC1 to MC20. To simplify the discussion, the example shown in Table 2 only includes a small number of users and pieces of media content. In practice, there is no limit on the number of users and pieces of media content that may be included in such kind of relationship matrix.
  • TABLE 2
    Relationship Matrix between Users and Media Content
    U1 U2 U3 U4 U5 U6 U7 U8
    MC1 3 3* 3
    MC2 3 4
    MC3 5 5 2
    MC4 2 2 2
    MC5 5 5 4 3
    MC6 3 2.5* 2 1
    MC7 1 1 1 1 1
    MC8 5
    MC9 4 2
    MC10 1 3
    MC11 1 5 1
    MC12 3
    MC13 3 3 2
    MC14 3 3 3
    MC15 2
    MC16 1 1
    MC17 4 4 4 1
    MC18 4 5 3
    MC19 1
    MC20 4 4 4 1
  • In Table 2, suppose a numerical rating system having values between 1 and 5 is used, and if a particular user has rated a piece of media content, then the corresponding rating value is indicated in the appropriate cell (without “*”). For example, user U1 has rated media content MC1, MC3, MC4, MC5, MC7, MC9, MC14, MC15, MC16, and MC18. User U2 has rated media content MC3, MC5, MC9, MC11, MC13, and MC20. And so on. Conversely, an empty cell in the matrix table indicates that the user has not rated that particular piece of media content. For example, user U1 has not rated media content MC2, MC6, MC8, MC10, MC11, MC12, MC13, MC17, MC19, and MC20. And so on. Since in practice, the number of pieces of media content tend to be very large, it is unlikely for any user to have rated all available media content. Consequently, a rating matrix in practice often has many empty cells, i.e., missing ratings.
  • One way to estimate ratings for a particular user is to find patterns that indicate similarities between the ratings given by this user and ratings given by other users. For examples, users U1 and U5 have both given media content MC4, MC14, and MC16 the same ratings respectively, which suggests that these two users have similar preferences. User U5 has not rated media content MC1, but user U1 has given media content MC1 a rating value of 3. Thus, based on the existing rating data, it may be reasonable to estimate that user U5 would also give media content MC1 a rating value of 3, similar to the rating value given by user U1. The cell for U5 and MC1 may then be filled with the estimated rating value of 3. To distinguish a rating value actually assigned by a user from an estimated rating value, in Table 2, estimated rating values are marked with “*”.
  • In another example, users U3, U4, and U6 have all given media content MC7 and MC17 the same ratings respectively, which suggests that these three users have similar preferences. User U4 has not rated media content MC6, but both users U3 and U6 haven rated media content MC6, user U3 giving a rating value of 3 and user U6 giving a rating value of 2. Based on the existing rating data, it may be reasonable to estimate that user U4 would give media content MC6 a rating value that is the average of the rating values given by users U3 and U6, i.e., 2.5. Thus, by repeatedly finding similarities among the rating values given by the users, it may be possible to estimate a rating value for every user with respect to every piece of unrated media content in the matrix table. In other words, all the empty cells in the matrix table may eventually be filled with estimated rating values. Often, the larger the datasets, i.e., a very larger number of users and media content, the more accurate the estimated rating values.
  • There are different ways to combine ClipRank and Collaborative Filtering. According to one embodiment, ClipRank is first used to reduce the size of the dataset, i.e., the size of the matrix, and then Collaborative Filtering is used to estimate ratings for all the remaining media content.
  • As explained above, in practice, a dataset used for Collaborative Filtering often contains a very large number, such as thousands or millions, of data points. This means that a matrix such as the one shown in Table 2 often contains very large numbers of users and pieces of media content. It is unlikely that each individual user has rated all the available media content. In fact, it is more likely that an individual user has only rated a selected, small number of media content. Consequently, there is the need to estimate ratings for many pieces of media content for each user using Collaborative Filter, which may be very time-consuming.
  • One way to reduce the size of the dataset, and specifically the number of pieces of media content is to use ClipRank. FIG. 5 shows a method of combining Collaborative Filtering and ClipRank by using ClipRank to select a subset of media content used for Collaborative Filtering. First, the initial set of media content is ranked based on their ClipRank weights using the method described in FIG. 2 (step 510), i.e., constructing a relationship graph for the users and the pieces of media content, assigning initial weight values to the users and the media content, assigning predefined weight values to the relationships, repeatedly calculating the weight values associated with the users and the media content until the difference between the weight values calculated during the current iteration and the previous iteration is smaller than or equal to a predefined threshold value. Either general ClipRank weights or personalized ClipRank weights may be used.
  • Once the final ClipRank weights associated with the users and the media content are determined, the media content may be selected based on their final weights (step 520). For example, only media content having ClipRank weight values greater than or equal to a second predefined threshold is selected to be included in the matrix for Collaborative Filtering. By doing so, those pieces of media content having lower weights, i.e., lower ranking, are filtered out, since they do not appear to be popular among the users. Only pieces of media content having higher weights, i.e., higher ranking, are used for Collaborative Filtering. This reduces the size of the Collaborative Filtering matrix, and the time required to estimate ratings for the individual users.
  • Note that the higher the weight threshold, the less number of pieces of media content is selected for the Collaborative Filtering matrix, and vice versa. Thus, by carefully selecting the weight threshold, the size and complexity of the Collaborative Filtering matrix may be controlled.
  • A matrix is constructed using only those selected pieces of media content whose weights are greater than or equal to the weight threshold (step 530). And Collaborative Filtering is used to estimate the ratings for the individual users with respect to the pieces of media content in the matrix that the users have not rated (step 540).
  • According to another embodiment, Collaborative Filtering is used to obtain personalized ClipRank for a specific user, such that the ratings actually given to some pieces of media content by the user and the ratings estimated for other pieces of media content for the user using Collaborative Filtering are used as the initial weight values for calculating personalized ClipRank weights for the user.
  • Recall that personalized ClipRank is biased toward a particular user. The difference between personalized ClipRank and general ClipRank is that with personalized ClipRank, the initial weights assigned to the pieces of media content in the relationship graph are determined based on data associated with a particular user, whereas with general ClipRank, the initial weights assigned to the pieces of media content in the relationship graph are predefined default values, and usually the same value for all the media content.
  • Thus, ratings obtained using Collaborative Filtering for a particular user may be used as the initial weights assigned to the pieces of media content in the relationship graph to obtain personalized ClipRank for that user. FIG. 6 shows a method of combining Collaborative Filtering and ClipRank by using Collaborative Filtering to provide initial weight values for the media content in the relationship graph to obtain personalized ClipRank for a particular user.
  • Suppose there are a group of users and a set of media content. A Collaborative Filtering matrix, such as the one shown in Table 2, is constructed for the users and the media content. Usually, each user has given actual ratings to selected pieces of media content, but it is unlikely, although not impossible, that a single user has rated each and every piece of media content in the matrix. Using the matrix shown in Table 2 as an example, user U1 has given actual ratings to media content MC1, MC3, MC4, MC5, MC7, MC9, MC14, MC15, MC16, and MC18, but has not rated the other pieces of media content. However, ratings for the other pieces of media content, i.e., MC2, MC6, MC8, MC10, MC11, MC12, MC13, MC17, MC19, and MC20 may be estimated for user U1 using Collaborative Filtering (step 610).
  • Once the ratings for all the pieces of media content unrated by user U1 have been estimated, a relationship graph, such as the one shown in FIG. 1, may be constructed for the users and the media content (step 620). The initial weights assigned to the users may be predefined default values. Similarly the weights assigned to the relationships are also predefined values. However, the initial weights assigned to the media content are determined based on the ratings actually given by user U1 or estimated for user U1 in the Collaborative Filtering matrix (step 630). A direct correlation may be predefined between the rating values and the weight values. For example, the weight values may equal to the rating values, equal to the rating values multiplied by a factor, etc.
  • Then, the final ClipRank weights for the users and the media content may be calculated using the method shown in FIG. 2 (step 640), i.e., repeatedly calculating the weight values associated with the users and the media content until the difference between the weight values calculated during the current iteration and the previous iteration is smaller than or equal to a predefined threshold value. Since the initial weight values assigned to the media content in the relationship graph are determined based on Collaborative Filtering ratings obtained for a particular user, i.e., U1, the final ClipRank weights obtained in this case is biased toward user U1. In other words, the final ClipRank weights are personalized for user U1. The same method may be used to obtain personalized ClipRank weights for any other users.
  • Blending ClipRank and Collaborative Filtering
  • With respect to a set of media content, the pieces may be ranked based on their respective general ClipRank weights, personalized ClipRank weights, or Collaborative Filtering ratings. In addition, the results from general ClipRank, personalized ClipRank, and/or Collaborative Filtering may be blended in various ways. FIG. 7 shows a method of blending Collaborative Filtering, general ClipRank, and/or personalized ClipRank.
  • Suppose there is a set of media content associated with a group of users. First, the general ClipRank weights (step 710), the personalized ClipRank weights (step 712), and the Collaborative Filtering ratings (step 714) are calculated separately for the set of media content in connection with the group of users. Again, the general ClipRank weights and the personalized ClipRank weights may be calculated using the method shown in FIG. 2 or as slightly modified thereof, and the Collaborative Filtering ratings may be determined by performing any form of Collaborative Filtering algorithm on the set of media content and the group of users. Steps 710, 712, and 714 may be performed in any sequence or simultaneously, since each step does not depend on the results of the other two steps.
  • Next, optionally, the general ClipRank weights, the personalized ClipRank weights, and the Collaborative Filtering ratings obtained for the set of media content are normalized if they do not all have the same rating scales (step 720). For example, the general and personalized ClipRank weights may use a rating scale from 1 to 10, while the Collaborative Filtering ratings may use a rating scale from 1 to 100. In this case, the two rating scales need to be normalized to the same scale, e.g., by multiplying each ClipRank weight value by 10 or dividing each Collaborative Filtering rating value by 10, so that the three sets of numbers may be blended.
  • There are various ways to blend the general ClipRank weights, the personalized ClipRank weights, and/or the Collaborative Filtering ratings for the set of media content. For example, the general ClipRank weights and the Collaborative Filtering ratings may be blended. Alternatively, the personalized ClipRank weights and the Collaborative Filtering ratings may be blended. Finally, all three sets of numbers, i.e., the general ClipRank weights, the personalized ClipRank weights, and the Collaborative Filtering ratings, may be blended together.
  • According to one embodiment, the general ClipRank weights, the personalized ClipRank weights, and/or the Collaborative Filtering ratings are linearly blended to obtain final ratings for the set of media content (step 730).
  • First, to linearly blend the general ClipRank weight and the Collaborative Filtering rating for a piece of media content, a final rating for the piece of media content may be calculated using the following equation:

  • final rating=W 1*GCRW+W2*CFR;  (7)
  • where GCRW denotes the general ClipRank weight associated with the piece of media content, W1 denotes a blending weight giving to the general ClipRank weight, CFR denotes the Collaborative Filtering rating associated with the piece of media content, and W2 denotes a blending weight giving to the Collaborative Filtering rating.
  • Next, to linearly blend the personalized ClipRank weight and the Collaborative Filtering rating for a piece of media content, a final rating for the piece of media content may be calculated using the following equation:

  • final rating=W 1*PCRW+W2*CFR;  (8)
  • where PCRW denotes the personalized ClipRank weight associated with the piece of media content, W1 denotes a blending weight giving to the personalized ClipRank weight, CFR denotes the Collaborative Filtering rating associated with the piece of media content, and W2 denotes a blending weight giving to the Collaborative Filtering rating.
  • Finally, to linearly blend the general ClipRank weight, the personalized ClipRank weight, and the Collaborative Filtering rating for a piece of media content, a final rating for the piece of media content may be calculated using the following equation:

  • final rating=W 1*GCRW+W2*PCRW+W3*CFR;  (9)
  • where GCRW denotes the general ClipRank weight associated with the piece of media content, W1 denotes a blending weight giving to the general ClipRank weight, PCRW denotes the personalized ClipRank weight associated with the piece of media content, W2 denotes a blending weight giving to the personalized ClipRank weight, CFR denotes the Collaborative Filtering rating associated with the piece of media content, and W3 denotes a blending weight giving to the Collaborative Filtering rating.
  • The blending weights, Wi, given to the general and personalized ClipRank weight and the Collaborative Filtering rating control how much weight each variable contributes to the final rating in equations (7), (8), and (9). Thus, by adjusting the blending weights, the contributions of the three variables to the final rating may be adjusted. For example, in equation (9), if it is desirable for the final rating to be relatively more dependent on the general ClipRank weight, then the blending weight for the general ClipRank weight, W1, may be increased while the blending weights for the personalized ClipRank weight and the Collaborative Filtering rating, W2 and W3 respectively, may be decreased. Conversely, if it is desirable for the final rating to be relatively more dependent on the Collaborative Filtering rating, then the blending weight for the Collaborative Filtering rating, W3, may be increased while the blending weights for the general ClipRank weight and the personalized ClipRank weight, W1 and W2 respectively, may be decreased.
  • Once the final ratings have been calculated for all the pieces of media content consistently using any one of equations (7), (8), or (9), the set of media content may be ranked based on their respective final ratings (step 732).
  • According to another embodiment, first, a subset of media content is selected by choosing those pieces of media content from the original set of media content whose general or personalized ClipRank weights are greater than or equal to a predefined weight threshold (step 740). By adjusting the weight threshold, the number of pieces of media content selected for the subset of media content may be increased or decreased. Then, the subset of media content is ranked based on their respective Collaborative Filtering ratings (step 742).
  • According to another embodiment, first, a subset of media content is selected by choosing those pieces of media content from the original set of media content whose Collaborative Filtering ratings are greater than or equal to a predefined rating threshold (step 750). Again, by adjusting the rating threshold, the number of pieces of media content selected for the subset of media content may be increased or decreased. Then, the subset of media content is ranked based on their respective general or personalized ClipRank weights (step 752).
  • A set of media content may be ranked using general ClipRank, personalized ClipRank, Collaborative Filtering, or various combinations or blending of general ClipRank, personalized ClipRank, and/or Collaborative Filtering. There are many applications or situations where it is desirable rank a set of media content. One particular usage of these ranking methods is for recommending personalized video content. System and methods for providing personalized video content are described in more detail in co-pending U.S. patent application Ser. No. ______, (Attorney Docket No. SISAP021/CSL07-NW14-A), filed on ______, 2008 (concurrently herewith on the same day as the present application), entitled “A PERSONALIZED VIDEO SYSTEM” by Gibbs et al., which is hereby incorporated by reference in its entirety and for all intents and purposes. To summarize, information with respect to individual media devices are automatically monitored and collected, and such information is used to help select personalized video content for the users of the individual media devices. The selected video content may first be ranked using general or personalized ClipRank or combinations of ClipRank and Collaborative Filtering before being presented to the device users.
  • The methods of combining ClipRank and Collaborative Filtering, e.g., steps shown in FIGS. 5, 6 and 7, may be implemented as computer program product(s) having a set of computer program instructions. The computer program instructions with which embodiments of the invention are implemented may be stored in any type of computer-readable media, and may be executed according to a variety of computing models including, for example, on a consumer electronic device, on a stand-alone computing device, or according to a distributed computing model in which various of the functionalities described herein may be effected or employed at different locations.
  • FIGS. 8A and 8B illustrate a computer system 800 suitable for implementing embodiments of the present invention. FIG. 8A shows one possible physical form of the computer system. The computer program instructions implementing the various embodiments of the invention may be executed on such a computer system. Of course, the computer system may have many physical forms including an integrated circuit, a printed circuit board, a small handheld device (such as a mobile telephone or PDA), a personal computer or a super computer. Computer system 800 includes a monitor 802, a display 804, a housing 806, a disk drive 808, a keyboard 810 and a mouse 812. Disk 814 is a computer-readable medium used to transfer data to and from computer system 800.
  • FIG. 8B is an example of a block diagram for computer system 800. Attached to system bus 820 are a wide variety of subsystems. Processor(s) 822 (also referred to as central processing units, or CPUs) are coupled to storage devices including memory 824. Memory 824 includes random access memory (RAM) and read-only memory (ROM). As is well known in the art, ROM acts to transfer data and instructions uni-directionally to the CPU, and RAM is used typically to transfer data and instructions in a bi-directional manner. Both of these types of memories may include any suitable of the computer-readable media described below. A fixed disk 826 is also coupled bi-directionally to CPU 822; it provides additional data storage capacity and may also include any of the computer-readable media described below. Fixed disk 826 may be used to store programs, data and the like and is typically a secondary storage medium (such as a hard disk) that is slower than primary storage. It will be appreciated that the information retained within fixed disk 826, may, in appropriate cases, be incorporated in standard fashion as virtual memory in memory 824. Removable disk 828 may take the form of any of the computer-readable media described below.
  • CPU 822 is also coupled to a variety of input/output devices such as display 804, keyboard 810, mouse 812 and speakers 830. In general, an input/output device may be any of: video displays, track balls, mice, keyboards, microphones, touch-sensitive displays, transducer card readers, magnetic or paper tape readers, tablets, styluses, voice or handwriting recognizers, biometrics readers, or other computers. CPU 822 optionally may be coupled to another computer or telecommunications network using network interface 840. With such a network interface, it is contemplated that the CPU might receive information from the network, or might output information to the network in the course of performing the above-described method steps. Furthermore, method embodiments of the present invention may execute solely upon CPU 822 or may execute over a network such as the Internet in conjunction with a remote CPU that shares a portion of the processing.
  • While this invention has been described in terms of several preferred embodiments, there are alterations, permutations, and various substitute equivalents, which fall within the scope of this invention. It should also be noted that there are many alternative ways of implementing the methods and apparatuses of the present invention. For example, although the system and method are described in connection with ranking media content, the same concept may be applied to rank any objects or matters that have various types of relationships among themselves.
  • In addition, in the embodiment shown in FIG. 2, the weights associated with a user or a piece of media content are calculated based on the relationships directly and immediately connected with the user or the piece of media content, i.e., the first level relationships. However, it is possible to take into consideration subsequent levels of relationships, i.e., relationships further removed, when calculating the weights associated with a particular user or a particular piece of media content. For example, in the relationship graph shown in FIG. 1, node MC 123 is connected with node U 112 by edge R 147. Thus, node MC 123 only has one first level relationship, R 147. However, node U 112 has additional relationships, R 148, R 149, R 150, and R 157 with other nodes MC 122 and MC 127. These relationships are further removed from node MC 123, i.e., second level relationships, and are not directly connected with node MC 123. But since they are directly connected with node U 112, which is directly connected with node MC 123, their weights may also have some influence on the weights of node MC 123, although the influence may not be as strong as the influence asserted by the first level relationship R 147. Generally, the farther a relationship is removed from a node, the less influence its weight has on the weight associated with that node. Nevertheless, it is possible to define different formulas for calculating the weights associated with the nodes, i.e. users and media content, which take into consideration the weights associated with multiple levels of relationships.
  • Furthermore, they may be other ways to combine ClipRank and Collaborative Filtering. It is therefore intended that the following appended claims be interpreted as including all such alterations, permutations, and various substitute equivalents as fall within the true spirit and scope of the present invention.

Claims (36)

1. A method of combining ClipRank and Collaborative Filtering, comprising:
calculating ClipRank weights associated with a plurality of pieces of media content based on relationships among the plurality of pieces of media content and a plurality of users;
selecting from the plurality of pieces of media content those pieces having ClipRank weights greater than or equal to a predefined weight threshold to obtain a plurality of selected pieces of media content; and
performing Collaborative Filtering on the plurality of selected pieces of media content and plurality of users.
2. A method as recited in claim 1,
wherein each of the plurality pieces of media content has at least one relationship with at least one of the plurality of users and each of the plurality of users has at least one relationship with at least one of the plurality pieces of media content,
wherein each of the plurality pieces of media content is associated with a weight, each of the plurality of users is associated with a weight, and each relationship is associated with a weight, and
wherein calculating the ClipRank weights associated with the plurality of pieces of media content comprises for each of the plurality pieces of media content and each of the plurality of users, recursively calculating and updating the weight associated with the piece of media content or the user until a difference between the weights associated with the plurality pieces of media content and the plurality of users calculated during a current iteration and the weights associated with the plurality pieces of media content and the plurality of users calculated during a previous iteration is less than a predefined threshold, wherein the weight associated with a piece of media content or a user is calculated based on the weights of the at least one relationship and the weights of the at least one piece of media content or the at least one user with which the piece of media content or the user has the at least one relationship
3. A method as recited in claim 2, wherein weight associated with a piece of media content or a user equals
i = 1 i = n ( W i ( r ) * W i ( mc_u ) ) ,
wherein n denotes a total number of relationships the piece of media content or the user has with other pieces of media content or other users, Wi(r) denotes the weight associated with a relationship, relationship i, the piece of media content or the user has with another piece of media content or another user, and Wi(mc_u) denotes the weight associated with the corresponding other piece of media content, media content i, or the corresponding other user, user i, having the relationship, relationship i, with the piece of media content or the user.
4. A method as recited in claim 2, wherein calculating ClipRank weights associated with the plurality of pieces of media content further comprises:
assigning an initial value to each weight associated with each of the plurality pieces of media content;
assigning an initial value to each weight associated with each of the plurality of users; and
defining a value to each weight associated with each relationship.
5. A method as recited in claim 2, wherein the difference between the weights associated with the plurality pieces of media content and the plurality of users calculated during a current iteration and the weights associated with the plurality pieces of media content and the plurality of users calculated during a previous iteration equals
( i = 1 i = n W i , j ( mc_u ) - i = 1 i = n W i , ( j - 1 ) ( mc_u ) ) ,
where n denotes a total number of the plurality pieces of media content and the plurality of users, Wi,j(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, during the current iteration, iteration j, and Wi,(j-1)(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, during the previous iteration, iteration j−1.
6. A method as recited in claim 2, wherein the difference between the weights associated with the plurality pieces of media content and the plurality of users calculated during a current iteration and the weights associated with the plurality pieces of media content and the plurality of users calculated during a previous iteration equals
( i = 1 i = n W i , j ( mc_u ) n - i = 1 i = n W i , ( j - 1 ) ( mc_u ) n ) ,
where n denotes a total number of the plurality pieces of media content and the plurality of users, Wi,j(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, during the current iteration, iteration j, and Wi,(j-1)(mc_u) denotes the weight associated a piece of media content, media content i, or a user, user i, during the previous iteration, iteration j−1.
7. A method as recited in claim 1, wherein performing the Collaborative Filtering on the plurality of selected pieces of media content and the plurality of users comprises:
for each of the plurality of users, estimating ratings for those pieces of the plurality of selected pieces of media content that have not been rated by the user based on ratings given to other pieces of the plurality of selected pieces of media content by the user and selected ratings given to the plurality of selected pieces of media content by other of the plurality of users.
8. A method as recited in claim 1, further comprising:
adjusting a number of the plurality of selected pieces of media content by increasing or decreasing the predefined weight threshold.
9. A method as recited in claim 1, further comprising:
recommending at least one piece of the selected pieces of media content to one of the plurality of users based on at least one rating estimated for the at least one piece of media content for the user using Collaborative Filtering.
10. A method of combining ClipRank and Collaborative Filtering, comprising:
performing Collaborative Filtering on a plurality of pieces of media content and a plurality of users for one of the plurality of users; and
calculating personalized ClipRank weights associated with the plurality of pieces of media content for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user.
11. A method as recited in claim 10, wherein performing Collaborative Filtering on the plurality of pieces of media content and one of the plurality of users comprises estimating ratings for those pieces of the plurality of selected pieces of media content that have not rated by the user based on ratings given to other pieces of the plurality of selected pieces of media content by the user and selected ratings given to the plurality of selected pieces of media content by other of the plurality of users.
12. A method as recited in claim 10,
wherein each of the plurality pieces of media content has at least one relationship with at least one of a plurality of users and each of the plurality of users has at least one relationship with at least one of the plurality pieces of media content,
wherein each of the plurality pieces of media content is associated with a weight, each of the plurality of users is associated with a weight, and each relationship is associated with a weight, and
wherein calculating personalized ClipRank weights associated with the plurality of pieces of media content for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user comprises
assigning an initial value to the weight associated with each of the plurality pieces of media content, wherein an initial value of a weight associated with a piece of media content is determined based on a rating given to that piece of media content by the user or a rating estimated for that piece of media content for the user using Collaborative Filtering; and
for each of the plurality pieces of media content and each of the plurality of users, recursively calculating and updating the weight associated with the piece of media content or the user until the difference between the weights associated with the plurality pieces of media content and the plurality of users calculated during a current iteration and the weights associated with the plurality pieces of media content and the plurality of users calculated during a previous iteration is less than a predefined threshold, wherein the weight associated with a piece of media content or a user is calculated based on the weights of the at least one relationship and the weights of the at least one piece of media content or the at least one user with which the piece of media content or the user has the at least one relationship.
13. A method as recited in claim 12, wherein the weight associated with a piece of media content or a user equals
i = 1 i = n W ( r ) i * W ( mc_u ) i ,
where n denotes a total number of relationships the piece of media content or the user has with other pieces of media content or other users, W(r)i denotes the weight associated with a relationship the piece of media content or the user has with another piece of media content or another user, and W(mc_u)i denotes the weight associated with the corresponding other piece of media content or the corresponding other user having the relationship with the piece of media content or the user.
14. A method as recited in claim 10, further comprising:
recommending at least one piece of the plurality of pieces of media content to the users based on the personalized ClipRank weight associated with the at least one piece of media content.
15. A method of blending ClipRank and Collaborative Filtering results, comprising:
calculating general ClipRank weights associated with a plurality of pieces of media content based on relationships among the plurality of pieces of media content and a plurality of users;
determining Collaborative Filtering ratings associated with the plurality of pieces of media content in connection with the plurality of users; and
blending the general ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
16. A method as recited in claim 15, further comprising:
normalizing the general ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
17. A method as recited in claim 15, wherein blending the general ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
for each of the plurality of pieces of media content, calculate a final rating, such that the final rating for a piece of media content equals W1*GCRW+W2*CFR, wherein GCRW denotes the general ClipRank weight associated with the piece of media content, W1 denotes a blending weight giving to the general ClipRank weight, CFR denotes the Collaborative Filtering rating associated with the piece of media content, and W2 denotes a blending weight giving to the Collaborative Filtering rating.
18. A method as recited in claim 17, further comprising:
ranking the plurality of pieces of media content based on their respective final ratings.
19. A method as recited in claim 15, wherein blending the general ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
selecting from the plurality of pieces of media content those pieces having general ClipRank weights greater than or equal to a predefined weight threshold to obtain a plurality of selected pieces of media content; and
ranking the plurality of selected pieces of media content based on their respective Collaborative Filtering ratings.
20. A method as recited in claim 15, wherein blending the general ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
selecting from the plurality of pieces of media content those pieces having Collaborative Filtering ratings greater than or equal to a predefined rating threshold to obtain a plurality of selected pieces of media content; and
ranking the plurality of selected pieces of media content based on their respective general ClipRank weights.
21. A method as recited in claim 15, further comprising:
calculating personalized ClipRank weights associated with the plurality of pieces of media content based on relationships among the plurality of pieces of media content and the plurality of users for one of the plurality of users.
22. A method as recited in claim 21, further comprising:
normalizing the general ClipRank weights, the personalized ClipRank weights, and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
23. A method as recited in claim 21, further comprising:
blending the personalized ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
24. A method as recited in claim 23, wherein blending the personalized ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
for each of the plurality of pieces of media content, calculate a final rating, such that the final rating for a piece of media content equals W1*PCRW+W2*CFR, wherein PCRW denotes the personalized ClipRank weight associated with the piece of media content, W1 denotes a blending weight giving to the personalized ClipRank weight, CFR denotes the Collaborative Filtering rating associated with the piece of media content, and W2 denotes a blending weight giving to the Collaborative Filtering rating.
25. A method as recited in claim 24, further comprising:
ranking the plurality of pieces of media content based on their respective final ratings.
26. A method as recited in claim 23, wherein blending the personalized ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
selecting from the plurality of pieces of media content those pieces having personalized ClipRank weights greater than or equal to a predefined weight threshold to obtain a plurality of selected pieces of media content; and
ranking the plurality of selected pieces of media content based on their respective Collaborative Filtering ratings.
27. A method as recited in claim 23, wherein blending the personalized ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
selecting from the plurality of pieces of media content those pieces having Collaborative Filtering ratings greater than or equal to a predefined rating threshold to obtain a plurality of selected pieces of media content; and
ranking the plurality of selected pieces of media content based on their respective personalized ClipRank weights.
28. A method as recited in claim 21, further comprising:
blending the generalized ClipRank weights, the personalized ClipRank weights and, the Collaborative Filtering ratings associated with the plurality of pieces of media content.
29. A method as recited in claim 28, wherein blending the generalized ClipRank weights, the personalized ClipRank weights and, the Collaborative Filtering ratings associated with the plurality of pieces of media content comprises:
for each of the plurality of pieces of media content, calculate a final rating, such that the final rating for a piece of media content equals W1*GCRW+W2*PCRW+W3*CFR, wherein GCRW denotes the general ClipRank weight associated with the piece of media content, W1 denotes a blending weight giving to the general ClipRank weight, PCRW denotes the personalized ClipRank weight associated with the piece of media content, W2 denotes a blending weight giving to the personalized ClipRank weight, CFR denotes the Collaborative Filtering rating associated with the piece of media content, and W3 denotes a blending weight giving to the Collaborative Filtering rating.
30. A method as recited in claim 29, further comprising:
ranking the plurality of pieces of media content based on their respective final ratings.
31. A computer program product for combining ClipRank and Collaborative Filtering, the computer program product comprising a computer-readable medium having a plurality of computer program instructions stored therein, which are operable to cause at least one computing device to:
calculate ClipRank weights associated with a plurality of pieces of media content based on relationships among the plurality of pieces of media content and a plurality of users;
select from the plurality of pieces of media content those pieces having ClipRank weights greater than or equal to a predefined weight threshold to obtain a plurality of selected pieces of media content; and
perform Collaborative Filtering on the plurality of selected pieces of media content and plurality of users.
32. A computer program product for combining ClipRank and Collaborative Filtering, the computer program product comprising a computer-readable medium having a plurality of computer program instructions stored therein, which are operable to cause at least one computing device to:
perform Collaborative Filtering on a plurality of pieces of media content and a plurality of users for one of the plurality of users; and
calculate personalized ClipRank weights associated with the plurality of pieces of media content for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user.
33. A computer program product for blending ClipRank and Collaborative Filtering results, the computer program product comprising a computer-readable medium having a plurality of computer program instructions stored therein, which are operable to cause at least one computing device to:
calculate general ClipRank weights associated with a plurality of pieces of media content based on relationships among the plurality of pieces of media content and a plurality of users;
determine Collaborative Filtering ratings associated with the plurality of pieces of media content in connection with the plurality of users; and
blend the general ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
34. A computer program product as recited in claim 33, wherein the plurality of computer program instructions are further operable to cause at least one computing device to:
calculate personalized ClipRank weights associated with the plurality of pieces of media content based on relationships among the plurality of pieces of media content and the plurality of users for one of the plurality of users.
35. A computer program product as recited in claim 34, wherein the plurality of computer program instructions are further operable to cause at least one computing device to:
blend the personalized ClipRank weights and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
36. A computer program product as recited in claim 34, wherein the plurality of computer program instructions are further operable to cause at least one computing device to:
blend the generalized ClipRank weights, the personalized ClipRank weights, and the Collaborative Filtering ratings associated with the plurality of pieces of media content.
US12/120,211 2007-11-20 2008-05-13 Combination of collaborative filtering and cliprank for personalized media content recommendation Active 2029-11-04 US8010536B2 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US98941307P true 2007-11-20 2007-11-20
US12/120,211 US8010536B2 (en) 2007-11-20 2008-05-13 Combination of collaborative filtering and cliprank for personalized media content recommendation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US12/120,211 US8010536B2 (en) 2007-11-20 2008-05-13 Combination of collaborative filtering and cliprank for personalized media content recommendation

Publications (2)

Publication Number Publication Date
US20090132520A1 true US20090132520A1 (en) 2009-05-21
US8010536B2 US8010536B2 (en) 2011-08-30

Family

ID=40643043

Family Applications (4)

Application Number Title Priority Date Filing Date
US12/120,217 Expired - Fee Related US8001561B2 (en) 2007-11-20 2008-05-13 System and method for automatically rating video content
US12/120,203 Active 2029-11-01 US8789108B2 (en) 2007-11-20 2008-05-13 Personalized video system
US12/120,211 Active 2029-11-04 US8010536B2 (en) 2007-11-20 2008-05-13 Combination of collaborative filtering and cliprank for personalized media content recommendation
US12/120,209 Active 2029-11-11 US8015192B2 (en) 2007-11-20 2008-05-13 Cliprank: ranking media content using their relationships with end users

Family Applications Before (2)

Application Number Title Priority Date Filing Date
US12/120,217 Expired - Fee Related US8001561B2 (en) 2007-11-20 2008-05-13 System and method for automatically rating video content
US12/120,203 Active 2029-11-01 US8789108B2 (en) 2007-11-20 2008-05-13 Personalized video system

Family Applications After (1)

Application Number Title Priority Date Filing Date
US12/120,209 Active 2029-11-11 US8015192B2 (en) 2007-11-20 2008-05-13 Cliprank: ranking media content using their relationships with end users

Country Status (1)

Country Link
US (4) US8001561B2 (en)

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090287651A1 (en) * 2008-05-14 2009-11-19 At&T Intellectual Property I, L.P. Management of Multimedia Content
US20090313295A1 (en) * 2008-06-11 2009-12-17 Blaxland Thomas A System and Process for Connecting Media Content
US20100205041A1 (en) * 2009-02-12 2010-08-12 Samsung Electronics Co., Ltd. Determining the interest of individual entities based on a general interest
US20100332426A1 (en) * 2009-06-30 2010-12-30 Alcatel Lucent Method of identifying like-minded users accessing the internet
US20110035683A1 (en) * 2009-08-07 2011-02-10 Larry Stead Method and apparatus for synchronous, collaborative media consumption
US20110066613A1 (en) * 2009-09-17 2011-03-17 Berkman Omer Syndicated Data Stream Content Provisioning
US20110093473A1 (en) * 2009-10-21 2011-04-21 At&T Intellectual Property I, L.P. Method and apparatus for staged content analysis
US20110131507A1 (en) * 2009-12-02 2011-06-02 Microsoft Corporation Personification of Software Agents
US20110231405A1 (en) * 2010-03-17 2011-09-22 Microsoft Corporation Data Structures for Collaborative Filtering Systems
US20110314030A1 (en) * 2010-06-22 2011-12-22 Microsoft Corporation Personalized media charts
US20120233183A1 (en) * 2011-03-08 2012-09-13 Sony Corporation Information processing apparatus, terminal apparatus, information presentation system, calculation method of evaluation scores, and program
WO2014004351A1 (en) * 2012-06-24 2014-01-03 Google Inc. Recommended content for an endorsement user interface
US20140012924A1 (en) * 2012-07-06 2014-01-09 Research In Motion Limited System and Method for Providing Application Feedback
US20140033265A1 (en) * 2008-07-07 2014-01-30 Bennett Leeds Digital rights management in a collaborative environment
US20140074648A1 (en) * 2012-09-11 2014-03-13 Google Inc. Portion recommendation for electronic books
US20140164365A1 (en) * 2012-12-11 2014-06-12 Facebook, Inc. Selection and presentation of news stories identifying external content to social networking system users
US9026592B1 (en) 2011-10-07 2015-05-05 Google Inc. Promoting user interaction based on user activity in social networking services
US9177065B1 (en) 2012-02-09 2015-11-03 Google Inc. Quality score for posts in social networking services
US9183259B1 (en) * 2012-01-13 2015-11-10 Google Inc. Selecting content based on social significance
US9223835B1 (en) 2012-01-24 2015-12-29 Google Inc. Ranking and ordering items in stream
US9454519B1 (en) 2012-08-15 2016-09-27 Google Inc. Promotion and demotion of posts in social networking services
US20170109444A1 (en) * 2015-10-20 2017-04-20 Adobe Systems Incorporated Personalized Recommendations Using Localized Regularization
US20170323017A1 (en) * 2016-05-07 2017-11-09 Muhammad Mashhood Alam Smart User Ratings System for User-relevant Filtered Ratings
US10032208B2 (en) * 2015-12-15 2018-07-24 International Business Machines Corporation Identifying recommended electronic books with detailed comparisons

Families Citing this family (121)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8732154B2 (en) 2007-02-28 2014-05-20 Samsung Electronics Co., Ltd. Method and system for providing sponsored information on electronic devices
US20080221989A1 (en) * 2007-03-09 2008-09-11 Samsung Electronics Co., Ltd. Method and system for providing sponsored content on an electronic device
US8001561B2 (en) * 2007-11-20 2011-08-16 Samsung Electronics Co., Ltd. System and method for automatically rating video content
US9088808B1 (en) 2008-02-08 2015-07-21 Google Inc. User interaction based related videos
US8055655B1 (en) 2008-02-15 2011-11-08 Google Inc. User interaction based related digital content items
US8359225B1 (en) * 2008-02-26 2013-01-22 Google Inc. Trust-based video content evaluation
US20090265389A1 (en) * 2008-03-28 2009-10-22 24Eight Learned cognitive system
US9892028B1 (en) * 2008-05-16 2018-02-13 On24, Inc. System and method for debugging of webcasting applications during live events
US7949678B2 (en) * 2008-05-20 2011-05-24 Samsung Electronics Co., Ltd. System and method for facilitating access to audio/visual content on an electronic device
US20090319379A1 (en) * 2008-06-20 2009-12-24 Raymond Anthony Joao Digital television broadcasting apparatus and method for providing information in a digital television and internet convergent environment
US20090328104A1 (en) * 2008-06-26 2009-12-31 At&T Delaware Intellectual Property, Inc. Methods, systems, and computer products for personalized channel services
US20100016011A1 (en) * 2008-07-15 2010-01-21 Motorola, Inc. Method for Collecting Usage Information on Wireless Devices for Ratings Purposes
US9710817B2 (en) * 2008-09-30 2017-07-18 Microsoft Technology Licensing, Llc Adaptive run-time advertisements
CN102124750B (en) * 2008-11-07 2014-10-08 汤姆逊许可证公司 Systems and methods for filtering content stream in multi-channel broadcasting multimedia system
US9357247B2 (en) 2008-11-24 2016-05-31 Time Warner Cable Enterprises Llc Apparatus and methods for content delivery and message exchange across multiple content delivery networks
US8487772B1 (en) 2008-12-14 2013-07-16 Brian William Higgins System and method for communicating information
US20100165123A1 (en) * 2008-12-29 2010-07-01 Microsoft Corporation Data-Driven Video Stabilization
US8856908B2 (en) * 2009-02-12 2014-10-07 Comcast Cable Communications, Llc Management and delivery of profile data
US8364671B1 (en) * 2009-02-23 2013-01-29 Mefeedia, Inc. Method and device for ranking video embeds
US8924527B2 (en) 2009-03-04 2014-12-30 Cisco Technology, Inc. Provisioning available network resources
JP2010273247A (en) * 2009-05-25 2010-12-02 Funai Electric Co Ltd Data transmission/reception system
US9153141B1 (en) 2009-06-30 2015-10-06 Amazon Technologies, Inc. Recommendations based on progress data
US9390402B1 (en) 2009-06-30 2016-07-12 Amazon Technologies, Inc. Collection of progress data
US8510247B1 (en) 2009-06-30 2013-08-13 Amazon Technologies, Inc. Recommendation of media content items based on geolocation and venue
US8813124B2 (en) 2009-07-15 2014-08-19 Time Warner Cable Enterprises Llc Methods and apparatus for targeted secondary content insertion
US20110029538A1 (en) * 2009-07-28 2011-02-03 Geosolutions B.V. System for creation of content with correlated geospatial and virtual locations by mobile device users
US20110041157A1 (en) * 2009-08-13 2011-02-17 Tandberg Television Inc. Systems and Methods for Selecting Content For a Subscriber of a Content Service Provider
US20110055260A1 (en) * 2009-08-31 2011-03-03 Cbs Interactive, Inc. Systems and methods for delivering a web page to a user in response to a page request
US8510769B2 (en) * 2009-09-14 2013-08-13 Tivo Inc. Media content finger print system
CN102033884A (en) * 2009-09-29 2011-04-27 国际商业机器公司 Method and system for processing service
US20110093329A1 (en) * 2009-10-13 2011-04-21 Robert Bodor Media preference consolidation and reconciliation
US8396055B2 (en) 2009-10-20 2013-03-12 Time Warner Cable Inc. Methods and apparatus for enabling media functionality in a content-based network
US10264029B2 (en) 2009-10-30 2019-04-16 Time Warner Cable Enterprises Llc Methods and apparatus for packetized content delivery over a content delivery network
US9635421B2 (en) 2009-11-11 2017-04-25 Time Warner Cable Enterprises Llc Methods and apparatus for audience data collection and analysis in a content delivery network
US8682145B2 (en) 2009-12-04 2014-03-25 Tivo Inc. Recording system based on multimedia content fingerprints
US9519728B2 (en) 2009-12-04 2016-12-13 Time Warner Cable Enterprises Llc Apparatus and methods for monitoring and optimizing delivery of content in a network
US20110213837A1 (en) * 2010-02-26 2011-09-01 Jason Beebe System and Method for Evaluating and Analyzing Content
US8522283B2 (en) * 2010-05-20 2013-08-27 Google Inc. Television remote control data transfer
US9204836B2 (en) 2010-06-07 2015-12-08 Affectiva, Inc. Sporadic collection of mobile affect data
US9642536B2 (en) 2010-06-07 2017-05-09 Affectiva, Inc. Mental state analysis using heart rate collection based on video imagery
US9503786B2 (en) 2010-06-07 2016-11-22 Affectiva, Inc. Video recommendation using affect
US8402495B1 (en) * 2010-06-07 2013-03-19 Purplecomm Inc. Content sequence technology
US9247903B2 (en) 2010-06-07 2016-02-02 Affectiva, Inc. Using affect within a gaming context
US9646046B2 (en) 2010-06-07 2017-05-09 Affectiva, Inc. Mental state data tagging for data collected from multiple sources
US10111611B2 (en) 2010-06-07 2018-10-30 Affectiva, Inc. Personal emotional profile generation
US9934425B2 (en) 2010-06-07 2018-04-03 Affectiva, Inc. Collection of affect data from multiple mobile devices
US9723992B2 (en) 2010-06-07 2017-08-08 Affectiva, Inc. Mental state analysis using blink rate
US10143414B2 (en) 2010-06-07 2018-12-04 Affectiva, Inc. Sporadic collection with mobile affect data
US9959549B2 (en) 2010-06-07 2018-05-01 Affectiva, Inc. Mental state analysis for norm generation
US10074024B2 (en) 2010-06-07 2018-09-11 Affectiva, Inc. Mental state analysis using blink rate for vehicles
US10204625B2 (en) 2010-06-07 2019-02-12 Affectiva, Inc. Audio analysis learning using video data
US8484511B2 (en) 2010-07-01 2013-07-09 Time Warner Cable Enterprises Llc Apparatus and methods for data collection, analysis and validation including error correction in a content delivery network
US9906838B2 (en) 2010-07-12 2018-02-27 Time Warner Cable Enterprises Llc Apparatus and methods for content delivery and message exchange across multiple content delivery networks
US9141982B2 (en) 2011-04-27 2015-09-22 Right Brain Interface Nv Method and apparatus for collaborative upload of content
US8433815B2 (en) 2011-09-28 2013-04-30 Right Brain Interface Nv Method and apparatus for collaborative upload of content
US8495683B2 (en) * 2010-10-21 2013-07-23 Right Brain Interface Nv Method and apparatus for content presentation in a tandem user interface
US8930979B2 (en) 2010-11-11 2015-01-06 Time Warner Cable Enterprises Llc Apparatus and methods for identifying and characterizing latency in a content delivery network
US10148623B2 (en) * 2010-11-12 2018-12-04 Time Warner Cable Enterprises Llc Apparatus and methods ensuring data privacy in a content distribution network
US20120144117A1 (en) * 2010-12-03 2012-06-07 Microsoft Corporation Recommendation based caching of content items
US8928760B2 (en) * 2010-12-07 2015-01-06 Verizon Patent And Licensing Inc. Receiving content and approving content for transmission
US9203539B2 (en) 2010-12-07 2015-12-01 Verizon Patent And Licensing Inc. Broadcasting content
US8982220B2 (en) * 2010-12-07 2015-03-17 Verizon Patent And Licensing Inc. Broadcasting content
US8453173B1 (en) * 2010-12-13 2013-05-28 Google Inc. Estimating demographic compositions of television audiences from audience similarities
US20120159540A1 (en) * 2010-12-16 2012-06-21 Electronics And Telecommunications Research Institute System and method for providing personalized content
US10089592B2 (en) 2010-12-29 2018-10-02 Comcast Cable Communications, Llc Measuring video asset viewing
US9621954B2 (en) * 2011-02-17 2017-04-11 Verizon Patent And Licensing Inc. Program guide including online channels
WO2012158234A2 (en) * 2011-02-27 2012-11-22 Affectiva, Inc. Video recommendation based on affect
GB2489675A (en) * 2011-03-29 2012-10-10 Sony Corp Generating and viewing video highlights with field of view (FOV) information
US9420320B2 (en) 2011-04-01 2016-08-16 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to estimate local market audiences of media content
CA2839481A1 (en) 2011-06-24 2012-12-27 The Directv Group, Inc. Method and system for obtaining viewing data and providing content recommendations at a set top box
US10055746B1 (en) 2011-06-24 2018-08-21 The Directv Group, Inc. Method and system for obtaining feedback for a content recommendation by various algorithms
US9788069B1 (en) * 2011-06-24 2017-10-10 The Directv Group, Inc. Method and system for recording recommended content within a user device
US9032451B2 (en) 2011-09-01 2015-05-12 The Directv Group, Inc. Method and system for using a second screen device for interacting with a set top box to enhance a user experience
US20130124539A1 (en) * 2011-09-13 2013-05-16 Airtime Media, Inc. Personal relevancy content resizing
US8719854B2 (en) 2011-10-28 2014-05-06 Google Inc. User viewing data collection for generating media viewing achievements
US9311679B2 (en) * 2011-10-31 2016-04-12 Hearsay Social, Inc. Enterprise social media management platform with single sign-on
US20130110803A1 (en) * 2011-11-02 2013-05-02 Microsoft Corporation Search driven user interface for navigating content and usage analytics
US9218417B2 (en) 2011-11-02 2015-12-22 Microsoft Technology Licensing, Llc Ad-hoc queries integrating usage analytics with search results
US9466065B2 (en) * 2011-11-02 2016-10-11 Microsoft Technology Licensing, Llc Integrating usage information with operation of a system
GB2497793A (en) * 2011-12-21 2013-06-26 Ninian Solutions Ltd Pre-emptive caching of potentially relevant content from a collaborative workspace at a client device
US8930354B2 (en) * 2012-01-09 2015-01-06 James Lewin System and method for organizing content
US9230212B2 (en) * 2012-02-02 2016-01-05 Peel Technologies, Inc. Content based recommendation system
EP2634707A1 (en) 2012-02-29 2013-09-04 British Telecommunications Public Limited Company Recommender control system, apparatus and method
US9467723B2 (en) 2012-04-04 2016-10-11 Time Warner Cable Enterprises Llc Apparatus and methods for automated highlight reel creation in a content delivery network
US9078040B2 (en) 2012-04-12 2015-07-07 Time Warner Cable Enterprises Llc Apparatus and methods for enabling media options in a content delivery network
US20130283330A1 (en) * 2012-04-18 2013-10-24 Harris Corporation Architecture and system for group video distribution
US9628573B1 (en) 2012-05-01 2017-04-18 Amazon Technologies, Inc. Location-based interaction with digital works
US9449089B2 (en) 2012-05-07 2016-09-20 Pixability, Inc. Methods and systems for identifying distribution opportunities
US8713606B2 (en) * 2012-05-14 2014-04-29 United Video Properties, Inc. Systems and methods for generating a user profile based customized media guide with user-generated content and non-user-generated content
TW201349157A (en) * 2012-05-18 2013-12-01 Richplay Information Co Ltd Electronic book classification method
US20130347014A1 (en) * 2012-06-24 2013-12-26 Disney Enterprises, Inc. Remote media ordering hub
CN104521243A (en) * 2012-08-08 2015-04-15 松下知识产权经营株式会社 Household electrical device, household electrical system, and server device
US9225930B2 (en) * 2012-08-09 2015-12-29 Universal Electronics Inc. System and method for a self adaptive multi-user program guide
CN103748585A (en) * 2012-08-17 2014-04-23 弗莱克斯电子有限责任公司 Intelligent Television
US9104708B2 (en) * 2012-09-07 2015-08-11 Magnet Systems, Inc. Managing activities over time in an activity graph
CN103002323A (en) * 2012-11-27 2013-03-27 中兴通讯股份有限公司 Method and terminal for sharing programs in interactive network television system
US9542060B1 (en) * 2012-12-13 2017-01-10 Amazon Technologies, Inc. User interface for access of content
US9131283B2 (en) 2012-12-14 2015-09-08 Time Warner Cable Enterprises Llc Apparatus and methods for multimedia coordination
KR20140101270A (en) * 2013-02-08 2014-08-19 삼성전자주식회사 Method and device for providing a recommendation panel, and method and sever for providing a recommendation item
US10003780B1 (en) 2013-03-14 2018-06-19 The Directv Group, Inc. Method and system for recording recommended content within a user device and indicating recording capacity
US10051024B2 (en) * 2013-03-14 2018-08-14 Charter Communications Operating, Llc System and method for adapting content delivery
US9405775B1 (en) * 2013-03-15 2016-08-02 Google Inc. Ranking videos based on experimental data
US20140317099A1 (en) * 2013-04-23 2014-10-23 Google Inc. Personalized digital content search
US9547698B2 (en) 2013-04-23 2017-01-17 Google Inc. Determining media consumption preferences
US9578258B2 (en) 2013-06-05 2017-02-21 V-Poll, Inc. Method and apparatus for dynamic presentation of composite media
US20150020106A1 (en) * 2013-07-11 2015-01-15 Rawllin International Inc. Personalized video content from media sources
US9891780B2 (en) * 2013-08-30 2018-02-13 Verizon Patent And Licensing Inc. User-based customization of a user interface
TWI524756B (en) * 2013-11-05 2016-03-01 Ind Tech Res Inst Method and device operable to store video and audio data
US9473796B1 (en) * 2013-12-31 2016-10-18 Google, Inc. Automated application of manually reviewed videos using matching
US20150199084A1 (en) * 2014-01-10 2015-07-16 Verizon Patent And Licensing Inc. Method and apparatus for engaging and managing user interactions with product or service notifications
GB2524073A (en) 2014-03-14 2015-09-16 Ibm Communication method and system for accessing media data
US9674579B1 (en) * 2014-03-31 2017-06-06 Google Inc. Rating videos based on parental feedback
US20150312609A1 (en) * 2014-04-28 2015-10-29 Rovi Guides, Inc. Methods and systems for preventing a user from terminating a service based on the accessibility of a preferred media asset
WO2015178919A1 (en) * 2014-05-22 2015-11-26 Hitachi, Ltd. Ranking documents in online enterprise social network
CN104102696A (en) * 2014-06-26 2014-10-15 海信集团有限公司 Content recommendation method and device
US10028025B2 (en) 2014-09-29 2018-07-17 Time Warner Cable Enterprises Llc Apparatus and methods for enabling presence-based and use-based services
FR3028631A1 (en) * 2014-11-17 2016-05-20 Groupe Canal+ Method for classifying content and recommendation contained in an electronic program guide
US10116676B2 (en) 2015-02-13 2018-10-30 Time Warner Cable Enterprises Llc Apparatus and methods for data collection, analysis and service modification based on online activity
US20160299992A1 (en) * 2015-04-09 2016-10-13 Yahoo!, Inc. Inductive matrix completion and graph proximity for content item recommendation
US9992539B2 (en) * 2016-04-05 2018-06-05 Google Llc Identifying viewing characteristics of an audience of a content channel
WO2018200406A1 (en) * 2017-04-24 2018-11-01 Harris Demetre Managing content using implicit weighted ratings

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060173974A1 (en) * 2005-02-02 2006-08-03 Victor Tang System and method for providing mobile access to personal media
US20080126303A1 (en) * 2006-09-07 2008-05-29 Seung-Taek Park System and method for identifying media content items and related media content items
US20080147649A1 (en) * 2001-01-10 2008-06-19 Looksmart, Ltd. Systems and methods of retrieving relevant information
US20080243812A1 (en) * 2007-03-30 2008-10-02 Microsoft Corporation Ranking method using hyperlinks in blogs

Family Cites Families (104)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5798785A (en) * 1992-12-09 1998-08-25 Discovery Communications, Inc. Terminal for suggesting programs offered on a television program delivery system
US5715445A (en) 1994-09-02 1998-02-03 Wolfe; Mark A. Document retrieval system employing a preloading procedure
US5758257A (en) * 1994-11-29 1998-05-26 Herz; Frederick System and method for scheduling broadcast of and access to video programs and other data using customer profiles
US5867226A (en) * 1995-11-17 1999-02-02 Thomson Consumer Electronics, Inc. Scheduler employing a predictive agent for use in a television receiver
FI104316B1 (en) * 1996-01-19 1999-12-31 Kauko Rautio A method and apparatus for processing tree trunks by chipping
US5983237A (en) 1996-03-29 1999-11-09 Virage, Inc. Visual dictionary
US5945988A (en) * 1996-06-06 1999-08-31 Intel Corporation Method and apparatus for automatically determining and dynamically updating user preferences in an entertainment system
US5948061A (en) 1996-10-29 1999-09-07 Double Click, Inc. Method of delivery, targeting, and measuring advertising over networks
US6177931B1 (en) * 1996-12-19 2001-01-23 Index Systems, Inc. Systems and methods for displaying and recording control interface with television programs, video, advertising information and program scheduling information
WO1998035303A1 (en) 1997-01-24 1998-08-13 The Board Of Regents Of The University Of Washington Method and system for network information access
IL121230A (en) * 1997-07-03 2004-05-12 Nds Ltd Intelligent electronic program guide
US20030088872A1 (en) * 1997-07-03 2003-05-08 Nds Limited Advanced television system
US5974406A (en) 1997-08-18 1999-10-26 International Business Machines Corporation Automated matching, scheduling, and notification system
CN1183746C (en) * 1998-03-04 2005-01-05 联合视频制品公司 Program guide system with monitoring of advertisement usage and user activities
US7117518B1 (en) * 1998-05-14 2006-10-03 Sony Corporation Information retrieval method and apparatus
US20050204388A1 (en) * 1998-06-11 2005-09-15 Knudson Edward B. Series reminders and series recording from an interactive television program guide
JP2000013708A (en) * 1998-06-26 2000-01-14 Hitachi Ltd Program selection aiding device
US6334127B1 (en) 1998-07-17 2001-12-25 Net Perceptions, Inc. System, method and article of manufacture for making serendipity-weighted recommendations to a user
ES2567261T3 (en) * 1998-07-17 2016-04-21 Rovi Guides, Inc. TV system with assisted search program by the user
US6898762B2 (en) * 1998-08-21 2005-05-24 United Video Properties, Inc. Client-server electronic program guide
TW465235B (en) * 1998-09-17 2001-11-21 United Video Properties Inc Electronic program guide with digital storage
US7110998B1 (en) 1998-10-13 2006-09-19 Virtual Gold, Inc. Method and apparatus for finding hidden patterns in the context of querying applications
GB9904662D0 (en) 1999-03-01 1999-04-21 Canon Kk Natural language search method and apparatus
US20010003214A1 (en) 1999-07-15 2001-06-07 Vijnan Shastri Method and apparatus for utilizing closed captioned (CC) text keywords or phrases for the purpose of automated searching of network-based resources for interactive links to universal resource locators (URL's)
JP4743740B2 (en) 1999-07-16 2011-08-10 マイクロソフト インターナショナル ホールディングス ビー.ブイ. To create an automated alternative content recommendation and system
US6774926B1 (en) 1999-09-03 2004-08-10 United Video Properties, Inc. Personal television channel system
US7509580B2 (en) * 1999-09-16 2009-03-24 Sharp Laboratories Of America, Inc. Audiovisual information management system with preferences descriptions
US8528019B1 (en) 1999-11-18 2013-09-03 Koninklijke Philips N.V. Method and apparatus for audio/data/visual information
US7840986B2 (en) * 1999-12-21 2010-11-23 Tivo Inc. Intelligent system and methods of recommending media content items based on user preferences
US6434747B1 (en) * 2000-01-19 2002-08-13 Individual Network, Inc. Method and system for providing a customized media list
US20030126597A1 (en) 2000-02-01 2003-07-03 Geoffrey Darby On-screen stripe and other methods for delivering information that facilitate convergence of audio/visual programming and advertisements with internet and other media usage
JP3718402B2 (en) 2000-03-07 2005-11-24 株式会社東芝 Information distribution system, an information providing device, the information storage device and information providing method
US7167895B1 (en) * 2000-03-22 2007-01-23 Intel Corporation Signaling method and apparatus to provide content on demand in a broadcast system
CA2870324C (en) * 2000-03-31 2017-08-15 United Video Properties, Inc. Systems and methods for improved audience measuring
US20020053084A1 (en) * 2000-06-01 2002-05-02 Escobar George D. Customized electronic program guide
US7552460B2 (en) * 2000-05-08 2009-06-23 Microsoft Corporation Modifying an electronic program guide based on viewer statistics
KR100420486B1 (en) 2000-07-08 2004-03-02 주식회사 라스이십일 System for providing network-based personalization service having a analysis function of user disposition
KR20020006810A (en) 2000-07-13 2002-01-26 장종옥 Methods and its System for Offering Information Through Intelligence Agent
GB2366478B (en) 2000-08-16 2005-02-09 Roke Manor Research Lan services delivery system
AU3980802A (en) 2000-10-20 2002-06-03 Wavexpress Inc System and method of providing relevant interactive content to a broadcast display
GB0026353D0 (en) 2000-10-27 2000-12-13 Canon Kk Apparatus and a method for facilitating searching
JP4587151B2 (en) 2000-12-27 2010-11-24 キヤノン株式会社 Internet dtv system, as well as commercial server and control method thereof
JP4686870B2 (en) 2001-02-28 2011-05-25 ソニー株式会社 Portable information terminal device, an information processing method, program recording medium, and program
TWI220036B (en) 2001-05-10 2004-08-01 Ibm System and method for enhancing broadcast or recorded radio or television programs with information on the world wide web
US7055165B2 (en) * 2001-06-15 2006-05-30 Intel Corporation Method and apparatus for periodically delivering an optimal batch broadcast schedule based on distributed client feedback
US7389307B2 (en) 2001-08-09 2008-06-17 Lycos, Inc. Returning databases as search results
US7296284B1 (en) * 2001-08-31 2007-11-13 Keen Personal Media, Inc. Client terminal for displaying ranked program listings based upon a selected rating source
US20030066090A1 (en) * 2001-09-28 2003-04-03 Brendan Traw Method and apparatus to provide a personalized channel
WO2003038563A2 (en) 2001-11-01 2003-05-08 Thomson Licensing S.A. Specific internet user target advertising replacement method and system
US6978470B2 (en) 2001-12-26 2005-12-20 Bellsouth Intellectual Property Corporation System and method for inserting advertising content in broadcast programming
US8001568B2 (en) * 2002-02-25 2011-08-16 Comcast Ip Holdings I, Llc Methods and systems for displaying recommended content alternatives
JP2004056462A (en) 2002-07-19 2004-02-19 Sony Corp Video image search assist method, video image search support device, and broadcast receiver
US7814512B2 (en) * 2002-09-27 2010-10-12 Microsoft Corporation Dynamic adjustment of EPG level of detail based on user behavior
US20040073924A1 (en) * 2002-09-30 2004-04-15 Ramesh Pendakur Broadcast scheduling and content selection based upon aggregated user profile information
JP4359810B2 (en) * 2002-10-01 2009-11-11 ソニー株式会社 User terminal, a data processing method, and program and data processing system,
US7124125B2 (en) 2002-11-01 2006-10-17 Loudeye Corp. System and method for providing media samples on-line in response to media related searches on the internet
US20040088375A1 (en) * 2002-11-01 2004-05-06 Sethi Bhupinder S. Method for prefetching Web pages to improve response time networking
KR20040052339A (en) 2002-12-16 2004-06-23 전자부품연구원 The targeting service method of 3D mesh content based on MPEG-4
EP1609312A4 (en) * 2003-04-03 2007-10-10 Sedna Patent Services Llc Content notification and delivery
KR100458460B1 (en) 2003-04-22 2004-11-26 엔에이치엔(주) A method of introducing advertisements and providing the advertisements by using access intentions of internet users and a system thereof
JP4661047B2 (en) 2003-05-30 2011-03-30 ソニー株式会社 Information processing apparatus and information processing method, and computer program
US20040268419A1 (en) 2003-06-24 2004-12-30 Microsoft Corporation Interactive content without embedded triggers
US7162473B2 (en) 2003-06-26 2007-01-09 Microsoft Corporation Method and system for usage analyzer that determines user accessed sources, indexes data subsets, and associated metadata, processing implicit queries based on potential interest to users
GB2403636A (en) 2003-07-02 2005-01-05 Sony Uk Ltd Information retrieval using an array of nodes
KR100493902B1 (en) * 2003-08-28 2005-06-10 삼성전자주식회사 Method And System For Recommending Contents
JP5059282B2 (en) 2003-10-14 2012-10-24 ソニー株式会社 Information providing system, information providing server, a user terminal device, content display device, a computer program, and a content display method
US9136956B2 (en) 2003-11-05 2015-09-15 Comcast Cable Holdings, Llc Method and system for planning and running video-on-demand advertising
US20050120391A1 (en) 2003-12-02 2005-06-02 Quadrock Communications, Inc. System and method for generation of interactive TV content
WO2005055196A2 (en) 2003-12-05 2005-06-16 Koninklijke Philips Electronics N.V. System & method for integrative analysis of intrinsic and extrinsic audio-visual data
US20050177555A1 (en) 2004-02-11 2005-08-11 Alpert Sherman R. System and method for providing information on a set of search returned documents
US20050216547A1 (en) 2004-03-10 2005-09-29 Foltz-Smith Russell A System for organizing advertisements on a web page and related method
KR100896245B1 (en) 2004-04-28 2009-05-08 후지쯔 가부시끼가이샤 Task computing
US7386542B2 (en) 2004-08-30 2008-06-10 The Mitre Corporation Personalized broadcast news navigator
KR20060027226A (en) 2004-09-22 2006-03-27 주식회사 타이거 시스템 아이엔씨 Customized portal-service system
US20060080321A1 (en) 2004-09-22 2006-04-13 Whenu.Com, Inc. System and method for processing requests for contextual information
JP4588395B2 (en) 2004-09-24 2010-12-01 富士通株式会社 Information processing terminal
US20060084430A1 (en) 2004-10-14 2006-04-20 Ng Eric M System and method for categorizing information into zones to determine delivery patterns
US20060123455A1 (en) * 2004-12-02 2006-06-08 Microsoft Corporation Personal media channel
US20060135156A1 (en) 2004-12-21 2006-06-22 Nokia Corporation Method and system for providing sponsored events for a mobile terminal
US8364670B2 (en) 2004-12-28 2013-01-29 Dt Labs, Llc System, method and apparatus for electronically searching for an item
WO2007004110A2 (en) 2005-06-30 2007-01-11 Koninklijke Philips Electronics N.V. System and method for the alignment of intrinsic and extrinsic audio-visual information
US20070038514A1 (en) 2005-08-12 2007-02-15 Macrovision Corporation Bid-based delivery of advertising promotions on internet-connected media players
US7882262B2 (en) 2005-08-18 2011-02-01 Cisco Technology, Inc. Method and system for inline top N query computation
JP4923778B2 (en) 2005-09-14 2012-04-25 カシオ計算機株式会社 Digital television receiving system, and the server device
US20070060109A1 (en) 2005-09-14 2007-03-15 Jorey Ramer Managing sponsored content based on user characteristics
US20070157220A1 (en) * 2005-12-29 2007-07-05 United Video Properties, Inc. Systems and methods for managing content
US20070154163A1 (en) * 2005-12-29 2007-07-05 United Video Properties, Inc. Systems and methods for creating aggregations of episodes of series programming in order
US20070186243A1 (en) * 2006-02-08 2007-08-09 Sbc Knowledge Ventures, Lp System and method of providing television program recommendations
US20070186234A1 (en) * 2006-02-09 2007-08-09 Christopher Cormack System and method for a ratings-based electronic guide
US8141114B2 (en) * 2006-02-28 2012-03-20 Microsoft Corporation Content ratings and recommendations
US20070214123A1 (en) 2006-03-07 2007-09-13 Samsung Electronics Co., Ltd. Method and system for providing a user interface application and presenting information thereon
WO2007127954A2 (en) * 2006-04-28 2007-11-08 Xanga.Com, Inc. Decentralized and fraud-resistant system and method for rating information content
US20070266403A1 (en) * 2006-05-15 2007-11-15 Sbc Knowledge Ventures, L.P. System and method for personalized video program listing and targeted content advertisement
US8561103B2 (en) * 2006-06-30 2013-10-15 At&T Intellectual Property Ii, L.P. Method and apparatus for providing a personalized television channel
US7590998B2 (en) 2006-07-27 2009-09-15 Sharp Laboratories Of America, Inc. Television system having internet web browsing capability
US20080046312A1 (en) 2006-08-15 2008-02-21 Ehud Shany Method and system for target marketing over the internet and interactive tv
US20080163059A1 (en) * 2006-12-28 2008-07-03 Guideworks, Llc Systems and methods for creating custom video mosaic pages with local content
US8732154B2 (en) 2007-02-28 2014-05-20 Samsung Electronics Co., Ltd. Method and system for providing sponsored information on electronic devices
US20080221989A1 (en) 2007-03-09 2008-09-11 Samsung Electronics Co., Ltd. Method and system for providing sponsored content on an electronic device
US8510453B2 (en) 2007-03-21 2013-08-13 Samsung Electronics Co., Ltd. Framework for correlating content on a local network with information on an external network
US20080250010A1 (en) 2007-04-05 2008-10-09 Samsung Electronics Co., Ltd. Method and system for determining and pre-processing potential user queries related to content in a network
US8209724B2 (en) 2007-04-25 2012-06-26 Samsung Electronics Co., Ltd. Method and system for providing access to information of potential interest to a user
US8671428B2 (en) * 2007-11-08 2014-03-11 Yahoo! Inc. System and method for a personal video inbox channel
US8001561B2 (en) 2007-11-20 2011-08-16 Samsung Electronics Co., Ltd. System and method for automatically rating video content

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080147649A1 (en) * 2001-01-10 2008-06-19 Looksmart, Ltd. Systems and methods of retrieving relevant information
US20060173974A1 (en) * 2005-02-02 2006-08-03 Victor Tang System and method for providing mobile access to personal media
US20080126303A1 (en) * 2006-09-07 2008-05-29 Seung-Taek Park System and method for identifying media content items and related media content items
US20080243812A1 (en) * 2007-03-30 2008-10-02 Microsoft Corporation Ranking method using hyperlinks in blogs

Cited By (45)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8356012B2 (en) * 2008-05-14 2013-01-15 At&T Intellectual Property I, L.P. Management of multimedia content
US8874521B2 (en) 2008-05-14 2014-10-28 At&T Intellectual Property I, L.P. Management of multimedia content
US9218328B2 (en) 2008-05-14 2015-12-22 At&T Intellectual Property I, L.P. Display of supplementary information on a graphical user interface
US20090287651A1 (en) * 2008-05-14 2009-11-19 At&T Intellectual Property I, L.P. Management of Multimedia Content
US20090313295A1 (en) * 2008-06-11 2009-12-17 Blaxland Thomas A System and Process for Connecting Media Content
US9489444B2 (en) 2008-06-11 2016-11-08 Comcast Cable Communications, Llc Determining associations between media objects
US8370396B2 (en) * 2008-06-11 2013-02-05 Comcast Cable Holdings, Llc. System and process for connecting media content
US8739242B2 (en) * 2008-07-07 2014-05-27 Adobe Systems Incorporated Digital rights management in a collaborative environment
US20140033265A1 (en) * 2008-07-07 2014-01-30 Bennett Leeds Digital rights management in a collaborative environment
US20100205041A1 (en) * 2009-02-12 2010-08-12 Samsung Electronics Co., Ltd. Determining the interest of individual entities based on a general interest
US20100332426A1 (en) * 2009-06-30 2010-12-30 Alcatel Lucent Method of identifying like-minded users accessing the internet
US20110035683A1 (en) * 2009-08-07 2011-02-10 Larry Stead Method and apparatus for synchronous, collaborative media consumption
US8782035B2 (en) * 2009-09-17 2014-07-15 My6Sense Inc. Syndicated data stream content provisioning
US20110066613A1 (en) * 2009-09-17 2011-03-17 Berkman Omer Syndicated Data Stream Content Provisioning
US8762397B2 (en) 2009-10-21 2014-06-24 At&T Intellectual Property I, Lp Method and apparatus for staged content analysis
US8332412B2 (en) * 2009-10-21 2012-12-11 At&T Intellectual Property I, Lp Method and apparatus for staged content analysis
US10140300B2 (en) 2009-10-21 2018-11-27 At&T Intellectual Property I, L.P. Method and apparatus for staged content analysis
US9305061B2 (en) 2009-10-21 2016-04-05 At&T Intellectual Property I, Lp Method and apparatus for staged content analysis
US20110093473A1 (en) * 2009-10-21 2011-04-21 At&T Intellectual Property I, L.P. Method and apparatus for staged content analysis
US8775935B2 (en) 2009-12-02 2014-07-08 Microsoft Corporation Personification of software agents
US20110131507A1 (en) * 2009-12-02 2011-06-02 Microsoft Corporation Personification of Software Agents
US20110231405A1 (en) * 2010-03-17 2011-09-22 Microsoft Corporation Data Structures for Collaborative Filtering Systems
US8560528B2 (en) 2010-03-17 2013-10-15 Microsoft Corporation Data structures for collaborative filtering systems
US8849816B2 (en) * 2010-06-22 2014-09-30 Microsoft Corporation Personalized media charts
US20110314030A1 (en) * 2010-06-22 2011-12-22 Microsoft Corporation Personalized media charts
CN102682153A (en) * 2011-03-08 2012-09-19 索尼公司 Information processing and presentation apparatus, terminal apparatus, calculation method of evaluation scores, and program
US20120233183A1 (en) * 2011-03-08 2012-09-13 Sony Corporation Information processing apparatus, terminal apparatus, information presentation system, calculation method of evaluation scores, and program
US9031941B2 (en) * 2011-03-08 2015-05-12 Sony Corporation Information processing apparatus, terminal apparatus, information presentation system, calculation method of evaluation scores, and program
US9313082B1 (en) 2011-10-07 2016-04-12 Google Inc. Promoting user interaction based on user activity in social networking services
US9026592B1 (en) 2011-10-07 2015-05-05 Google Inc. Promoting user interaction based on user activity in social networking services
US9183259B1 (en) * 2012-01-13 2015-11-10 Google Inc. Selecting content based on social significance
US9223835B1 (en) 2012-01-24 2015-12-29 Google Inc. Ranking and ordering items in stream
US9177065B1 (en) 2012-02-09 2015-11-03 Google Inc. Quality score for posts in social networking services
US10133765B1 (en) 2012-02-09 2018-11-20 Google Llc Quality score for posts in social networking services
WO2014004351A1 (en) * 2012-06-24 2014-01-03 Google Inc. Recommended content for an endorsement user interface
US9374396B2 (en) 2012-06-24 2016-06-21 Google Inc. Recommended content for an endorsement user interface
US20140012924A1 (en) * 2012-07-06 2014-01-09 Research In Motion Limited System and Method for Providing Application Feedback
US9454519B1 (en) 2012-08-15 2016-09-27 Google Inc. Promotion and demotion of posts in social networking services
US20140074648A1 (en) * 2012-09-11 2014-03-13 Google Inc. Portion recommendation for electronic books
US20140164365A1 (en) * 2012-12-11 2014-06-12 Facebook, Inc. Selection and presentation of news stories identifying external content to social networking system users
US10037538B2 (en) * 2012-12-11 2018-07-31 Facebook, Inc. Selection and presentation of news stories identifying external content to social networking system users
US20170109444A1 (en) * 2015-10-20 2017-04-20 Adobe Systems Incorporated Personalized Recommendations Using Localized Regularization
US10152545B2 (en) * 2015-10-20 2018-12-11 Adobe Systems Incorporated Personalized recommendations using localized regularization
US10032208B2 (en) * 2015-12-15 2018-07-24 International Business Machines Corporation Identifying recommended electronic books with detailed comparisons
US20170323017A1 (en) * 2016-05-07 2017-11-09 Muhammad Mashhood Alam Smart User Ratings System for User-relevant Filtered Ratings

Also Published As

Publication number Publication date
US8001561B2 (en) 2011-08-16
US20090133059A1 (en) 2009-05-21
US8015192B2 (en) 2011-09-06
US8789108B2 (en) 2014-07-22
US20090132519A1 (en) 2009-05-21
US20090133048A1 (en) 2009-05-21
US8010536B2 (en) 2011-08-30

Similar Documents

Publication Publication Date Title
US8996540B2 (en) User to user recommender
US7685132B2 (en) Automatic meta-data sharing of existing media through social networking
US8572169B2 (en) System, apparatus and method for discovery of music within a social network
US8799348B2 (en) Podcast organization and usage at a computing device
US7620551B2 (en) Method and apparatus for providing search capability and targeted advertising for audio, image, and video content over the internet
US8812580B2 (en) Override of automatically shared meta-data of media
Konstas et al. On social networks and collaborative recommendation
US7995505B2 (en) System and method for leveraging user rated media
US20080147711A1 (en) Method and system for providing playlist recommendations
US20060184558A1 (en) Recommender system for identifying a new set of media items responsive to an input set of media items and knowledge base metrics
US20160379316A1 (en) Prediction of user response to invitations in a social networking system based on keywords in the user's profile
US20080288494A1 (en) System Enabling Social Networking Through User-Generated Lists
EP1992006B1 (en) Collaborative structured tagging for item encyclopedias
Davidson et al. The YouTube video recommendation system
US8352331B2 (en) Relationship discovery engine
Liu et al. Online evolutionary collaborative filtering
US7890513B2 (en) Providing community-based media item ratings to users
US8301623B2 (en) Probabilistic recommendation system
US6321221B1 (en) System, method and article of manufacture for increasing the user value of recommendations
US7840563B2 (en) Collective ranking of digital content
US6334127B1 (en) System, method and article of manufacture for making serendipity-weighted recommendations to a user
US7970781B1 (en) Surfacing forums associated with a search string
EP1505521A2 (en) Setting user preferences for an electronic program guide
KR101318015B1 (en) System and method for playlist generation based on similarity data
KR101217421B1 (en) Media item clustering based on similarity data

Legal Events

Date Code Title Description
AS Assignment

Owner name: SAMSUNG ELECTRONICS CO., LTD., KOREA, DEMOCRATIC P

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:NEMETH, BOTTYAN;GIBBS, SIMON J.;SHESHAGIRI, MITHUN;AND OTHERS;REEL/FRAME:020967/0994

Effective date: 20080506

AS Assignment

Owner name: SAMSUNG ELECTRONICS CO., LTD., KOREA, REPUBLIC OF

Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE S COUNTRY TO READ --REPUBLIC OF KOREA-- PREVIOUSLY RECORDED ON REEL 020967 FRAME 0994;ASSIGNORS:NEMETH, BOTTYAN;GIBBS, SIMON J.;SHESHAGIRI, MITHUN;AND OTHERS;REEL/FRAME:022661/0724

Effective date: 20080506

Owner name: SAMSUNG ELECTRONICS CO., LTD., KOREA, REPUBLIC OF

Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE S COUNTRY TO READ --REPUBLIC OF KOREA-- PREVIOUSLY RECORDED ON REEL 020967 FRAME 0994. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT DOCUMENT;ASSIGNORS:NEMETH, BOTTYAN;GIBBS, SIMON J.;SHESHAGIRI, MITHUN;AND OTHERS;REEL/FRAME:022661/0724

Effective date: 20080506

FPAY Fee payment

Year of fee payment: 4